bibliography of genetic algorithms in arts and music
Post on 02-Jun-2018
215 Views
Preview:
TRANSCRIPT
8/10/2019 Bibliography of Genetic Algorithms in Arts and Music
http://slidepdf.com/reader/full/bibliography-of-genetic-algorithms-in-arts-and-music 1/49
An Indexed Bibliography of Genetic
Algorithms in Arts and Musiccompiled by
Jarmo T. Alander
Department of Electrical and Energy Engineering: Automation
University of Vaasa P.O. Box 700, FIN-65101 Vaasa, Finlandphone: +358-6-324 8444, fax: +358-6-324 8467
Report Series No. 94-1-ART (Updated 2014/05/06 11:37 )
available at http://www.uva.fi/~TAU/reports/report94-1/gaARTbib.pdf
8/10/2019 Bibliography of Genetic Algorithms in Arts and Music
http://slidepdf.com/reader/full/bibliography-of-genetic-algorithms-in-arts-and-music 2/49
Copyright c 1994-2013 Jarmo T. Alander
Cover image: c2010 Anssi Jantti, All rights reserved.GP producing graphics [1].
Trademarks
Product and company names listed are trademarks or trade names of their respective companies.
Warning
While this bibliography has been compiled with the utmost care, the editor takes no responsibility forany errors, missing information, the contents or quality of the references, nor for the usefulness and/orthe consequences of their application. The fact that a reference is included in this publication does notimply a recommendation. The use of any of the methods in the references is entirely at the user’s ownresponsibility. Especially the above warning applies to those references that are marked by trailing ’†’ (or’*’), which are the ones that the editor has unfortunately not had the opportunity to read. An abstractwas available of the references marked with ’*’.
8/10/2019 Bibliography of Genetic Algorithms in Arts and Music
http://slidepdf.com/reader/full/bibliography-of-genetic-algorithms-in-arts-and-music 3/49
Contents
1 Preface 1
1.1 Your contributions erroneous or missing? . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.1.1 How to cite this report? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2 How to get this report via Internet? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.3 Acknowledgement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
2 Introduction 4
3 Statistical summaries 53.1 Publication type . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53.2 Annual distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53.3 Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63.4 Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63.5 Geographical distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83.6 Conclusions and future . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
4 Indexes 9
4.1 Books . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94.2 Journal articles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94.3 Theses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
4.3.1 PhD theses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
4.3.2 Master’s theses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104.4 Report series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104.5 Patents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104.6 Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114.7 Subject index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144.8 Annual index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174.9 Geographical index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
5 Permuted title index 19
Bibliography 29
Appendixes 41
A Abbreviations 41
B Bibliography entry formats 42
i
8/10/2019 Bibliography of Genetic Algorithms in Arts and Music
http://slidepdf.com/reader/full/bibliography-of-genetic-algorithms-in-arts-and-music 4/49
ii
8/10/2019 Bibliography of Genetic Algorithms in Arts and Music
http://slidepdf.com/reader/full/bibliography-of-genetic-algorithms-in-arts-and-music 5/49
Chapter 1
Preface
“ Living organism are consummate problem solvers. They exhibit a versatilitythat puts the best computer programs to shame. ”
John H. Holland, [2]
The material of this bibliography has been extracted from the genetic algorithm bibliography [3], whichwhen this report was compiled (May 6, 2014) contained 22162 items and which has been collected fromseveral sources of genetic algorithm literature including Usenet newsgroup comp.ai.genetic and thebibliographies [4, 5, 6, 7]. The following index periodicals and databases have been used systematically
• A: International Aerospace Abstracts: Jan. 1995 – Sep. 1998
• ACM: ACM Guide to Computing Literature: 1979 – 1993/4
• BA: Biological Abstracts: July 1996 - Aug. 1998
• CA: Computer Abstracts: Jan. 1993 – Feb. 1995
• CCA: Computer & Control Abstracts: Jan. 1992 – Dec. 1999 (except May -95)
• ChA: Chemical Abstracts: Jan. 1997 - Dec. 2000
• CTI: Current Technology Index Jan./Feb. 1993 – Jan./Feb. 1994
• DAI: Dissertation Abstracts International: Vol. 53 No. 1 – Vol. 56 No. 10 (Apr. 1996)
• EEA: Electrical & Electronics Abstracts: Jan. 1991 – Apr. 1998
• EI A: The Engineering Index Annual: 1987 – 1992
• EI M: The Engineering Index Monthly: Jan. 1993 – Apr. 1998 (except May 1997)
• Esp@cenet patents – Apr. 2002
• IEEE: IEEE and IEE Journals – Fall 2002
• N: Scientific and Technical Aerospace Reports: Jan. 1993 - Dec. 1995 (except Oct. 1995)
• NASA NASA ADS www bibliography database: – Dec. 2002
• P: Index to Scientific & Technical Proceedings: Jan. 1986 – Dec 1999 (except Nov. 1994)
• PA: Physics Abstracts: Jan. 1997 – June 1999
• PubMed: National Library of Medicine Jan. 2000 – Oct. 2000
• SPIE Web The International Society for Optical Engineering – June 2002
1
8/10/2019 Bibliography of Genetic Algorithms in Arts and Music
http://slidepdf.com/reader/full/bibliography-of-genetic-algorithms-in-arts-and-music 6/49
2 Genetic algorithms in arts and music
1.1 Your contributions erroneous or missing?
The bibliography database is updated on a regular basis and certainly contains many errors and incon-sistences. The editor would be glad to hear from any reader who notices any errors, missing information,articles etc. In the future a more complete version of this bibliography will be prepared for the geneticalgorithms in arts and music research community and others who are interested in this rapidly growingarea of genetic algorithms.
When submitting updates to the database, paper copies of already published contributions are pre-ferred. Paper copies (or ftp ones) are needed mainly for indexing. We are also doing reviews of differentaspects and applications of GAs where we need as complete as possible collection of GA papers. Please,do not forget to include complete bibliographical information: copy also proceedings volume title pages,
journal table of contents pages, etc. Observe that there exists several versions of each subbibliography,therefore the reference numbers are not unique and should not be used alone in communi-
cation, use the key appearing as the last item of the reference entry instead.Complete bibliographical information is really helpful for those who want to find your contribution
in their libraries. If your paper was worth writing and publishing it is certainly worth to be referencedright in a bibliographical database read daily by GA researchers, both newcomers and established ones.
1.1.1 How to cite this report?
You can use the BiBTEX file GASUB.bib, which is available in our site lipas.uwasa.fi in directoryreports/report94-1 and contains records for GA subbibliographies for citing with LATEX/BibTEX.
1.2 How to get this report via Internet?
Versions of this bibliography are available via www from the following site:
media country site directory file web Finland lipas.uwasa.fi ~TAU/reports/report94-1 gaARTbib.pdf
The directory also contains some other indexed GA bibliographies shown in table B.1. In case you do
not find a proper one please let us know: it may be easy to tailor a new one.
1.3 Acknowledgement
The editor wants to acknowledge all who have kindly supplied references, papers and other informationon genetic algorithms in arts and music literature. At least the following GA researchers have alreadykindly supplied their complete autobibliographies and/or proofread references to their papers: DanAdler, Patrick Argos, Jarmo T. Alander, James E. Baker, Wolfgang Banzhaf, Helio J. C. Barbosa,Hans-Georg Beyer, Christian Bierwirth, Peter Bober Joachim Born, Ralf Bruns, I. L. Bukatova, ThomasBack, Chhandra Chakraborti, Nirupam Chakraborti, David E. Clark, Carlos A. Coello Coello, YuvalDavidor, Dipankar Dasgupta, Marco Dorigo, J. Wayland Eheart, Bogdan Filipic, Terence C. Fogarty,David B. Fogel, Toshio Fukuda, Hugo de Garis, Robert C. Glen, David E. Goldberg, Martina Gorges-
Schleuter, Hitoshi Hemmi, Vasant Honavar, Jeffrey Horn, Aristides T. Hatjimihail, Heikki HyotyniemiMark J. Jakiela, Richard S. Judson, Bryant A. Julstrom, Charles L. Karr, Akihiko Konagaya, AaronKonstam, John R. Koza, Kristinn Kristinsson, Malay K. Kundu, D. P. Kwok, Jouni Lampinen, JormaLaurikkala, Gregory Levitin, Carlos B. Lucasius, Timo Mantere, Michael de la Maza, John R. McDonnell,J. J. Merelo, Laurence D. Merkle, Zbigniew Michalewics, Melanie Mitchell, David J. Nettleton, VolkerNissen, Ari Nissinen, Tatsuya Niwa, Tomasz Ostrowski, Kihong Park, Jakub Podgorski, Timo Poranen,Nicholas J. Radcliffe, Colin R. Reeves, Gordon Roberts, David Rogers, David Romero, Sam Sandqvist,Ivan Santibanez-Koref, Marc Schoenauer, Markus Schwehm, Hans-Paul Schwefel, Michael T. Semertzidis,Davil L. Shealy, Moshe Sipper, William M. Spears, Donald S. Szarkowicz, El-Ghazali Talbi, MasahiroTanaka, Leigh Tesfatsion, Peter M. Todd, Marco Tomassini, Andrew L. Tuson, Kanji Ueda, Jari Vaario,
8/10/2019 Bibliography of Genetic Algorithms in Arts and Music
http://slidepdf.com/reader/full/bibliography-of-genetic-algorithms-in-arts-and-music 7/49
Acknowledgement 3
Gilles Venturini, Hans-Michael Voigt, Roger L. Wainwright, D. Eric Walters, James F. Whidborne, StefanWiegand, Steward W. Wilson, Xin Yao, Xiaodong Yin, and Ljudmila A. Zinchenko.
The editor also wants to acknowledge Elizabeth Heap-Talvela for her kind proofreading of the manuscriptof this bibliography and Tea Ollanketo and Sakari Kauvosaari for updating the database. Prof. TimoSalmi and the Computer Centre of University of Vaasa is acknowledged for providing and managing theonline web site lipas.uwasa.fi, where these indexed bibliographies are located since Summer 2012.
8/10/2019 Bibliography of Genetic Algorithms in Arts and Music
http://slidepdf.com/reader/full/bibliography-of-genetic-algorithms-in-arts-and-music 8/49
Chapter 2
Introduction
“Many scientist, possibly most scientist, just do science without thinking toomuch about it. They run experiments, make observations, show how certaindata conflict with more general views, set out theories, and so on. Periodically,however, some of us—scientists included—step back and look at what is goingon in science.”
David L., Hull, [8]
The table 2.1 gives the queries that have been used to extract this bibliography. The query system as wellas the indexing tools used to compile this report from the BiBTEX-database [9] have been implementedby the author mainly as sets of simple awk and gawk programs [10, 11].
string field class ,art, ANNOTE Art,art ANNOTE Artaesthetics ANNOTE Aesthetics,architecture ANNOTE Architecture music ANNOTE Musicanimation ANNOTE Cartoons
computer graphics ANNOTE Computer graphicsAudio JOURNAL Audio journal
Table 2.1: Queries used to extract this subbibliography from the source database.
You might also find bibliographies [12] containing references to image processing, [13] ontaining refer-ences to signal processing, and [14] containing references to computer aided design applications interesting.
4
8/10/2019 Bibliography of Genetic Algorithms in Arts and Music
http://slidepdf.com/reader/full/bibliography-of-genetic-algorithms-in-arts-and-music 9/49
Chapter 3
Statistical summaries
This chapter gives some general statistical sum-maries of genetic algorithms in arts and music lit-erature. More detailed indexes can be found inthe next chapter.
References to each class (c.f table 2.1) are listed
below:
• Aesthetics 3 references ([15]-[17])
• Architecture 12 references ([18]-[29])
• Art 33 references ([30]-[62])
• Audio journal 14 references ([63]-[76])
• Cartoons 16 references ([77]-[92])
• Computer graphics 60 references ([93]-
[151])
• Music 34 references ([152]-[185])
Observe that each reference is included (by thecomputer) only to one of the above classes (see thequeries for classification in table 2.1; the textualorder in the query gives priority for classes).
3.1 Publication type
This bibliography contains published contributions
including reports and patents. All unpublishedmanuscripts have been omitted unless acceptedfor publication. In addition theses, PhD, MScetc., are also included whether or not publishedsomewhere.
Table 3.1 gives the distribution of publicationtype of the whole bibliography. Observe that thenumber of journal articles may also include ar-ticles published or to be published in unknownforums.
type number of items
book 4section of a book 1part of a collection 1
journal article 62proceedings article 86report 4PhD thesis 6MSc thesis 2others 7total 173
Table 3.1: Distribution of publication type.
3.2 Annual distribution
Table 3.2 gives the number of genetic algorithmsin arts and music papers published annually. Theannual distribution is also shown in fig. 3.1. Theaverage annual growth of GA papers has been ap-proximately 40 % during late 70’s - early 90’s.
year items year items
1989 2 1990 31991 7 1992 41993 10 1994 171995 28 1996 201997 18 1998 111999 8 2000 62001 6 2002 10
2003 8 2004 02005 2 2006 42007 4 2008 32009 0 2010 12011 0 2012 1total 173
Table 3.2: Annual distribution of contributions.
5
8/10/2019 Bibliography of Genetic Algorithms in Arts and Music
http://slidepdf.com/reader/full/bibliography-of-genetic-algorithms-in-arts-and-music 10/49
8/10/2019 Bibliography of Genetic Algorithms in Arts and Music
http://slidepdf.com/reader/full/bibliography-of-genetic-algorithms-in-arts-and-music 11/49
Authors 7
1
10
100
1000number of papers(log scale)
1960 1970 1980 1990 2000 2010
year
Genetic algorithms in arts and music
2014/05/06
Figure 3.1: The number of papers applying ge-
netic algorithms in arts and music (•, N =174 ) and total GA papers (◦, N = 22162 ). Ob-serve that the last few years are most incompletein the database.
8/10/2019 Bibliography of Genetic Algorithms in Arts and Music
http://slidepdf.com/reader/full/bibliography-of-genetic-algorithms-in-arts-and-music 12/49
8 Genetic algorithms in arts and music
3.5 Geographical distribution
Table 3.5 gives the geographical distribution of authors, when the country of the author was known. Over80% of the references of the GA source database are classified by country.
2014/05/06 special comparison all country n % δ [%] ∆[%] N %
Total 166 100.00 20961 100.00United States 43 25.90 −0.90 −3 5618 26.80United Kingdom 27 16.27 +6.28 +63 2095 9.99Japan 18 10.84 −0.95 −8 2472 11.79Germany 16 9.64 +2.98 +45 1395 6.66Finland 12 7.23 +3.08 +74 870 4.15China 11 6.63 +1.38 +26 1100 5.25Spain 9 5.42 +3.35 +162 434 2.07Canada 5 3.01 +1.40 +87 337 1.61Portugal 4 2.41 +1.89 +363 110 0.52Taiwan 4 2.41 +0.17 +8 470 2.24Australia 3 1.81 −0.63 −26 511 2.44Hungary 3 1.81 +1.53 +546 59 0.28South Korea 3 1.81 −0.40 −18 464 2.21Cuba 2 1.20 +1.17 +3900 7 0.03France 2 1.20 −1.38 −53 541 2.58Austria 1 0.60 +0.00 +0 126 0.60Denmark 1 0.60 +0.31 +107 60 0.29Italy 1 0.60 −2.25 −79 598 2.85New Zealand 1 0.60 +0.45 +300 32 0.15Singapore 1 0.60 −0.21 −26 169 0.81Others 1 0.60 +0.50 +500 21 0.10
Table 3.5: The geographical distribution of the authors working on genetic algorithms in arts and music(n) compared (δ and ∆) to all authors in the field of GAs (N ). In the comparison column: δ % =%special −%all and ∆ = (1− nN Total
NnTotal)× 100%. ∆ is the relative (%) deviation from the expected number
of special papers. Observe that joint papers may have authors from several countries and that not allauthors have been attributed to a country.
3.6 Conclusions and future
The editor believes that this bibliography contains references to most genetic algorithms in arts andmusic contributions upto and including the year 1998 and the editor hopes that this bibliography couldgive some help to those who are working or planning to work in this rapidly growing area of geneticalgorithms.
8/10/2019 Bibliography of Genetic Algorithms in Arts and Music
http://slidepdf.com/reader/full/bibliography-of-genetic-algorithms-in-arts-and-music 13/49
Chapter 4
Indexes
4.1 Books
The following list contains all items classified asbooks.
Principia Evolvica, Simulierte Evolution mit Mathematica,[135]
Evolutionary Art and Computers, [57]
Evolving images, [53]
Illustrating Evolutionary Computation with Mathematica,[96]
total 4 books
4.2 Journal articlesThe following list contains the references to every
journal article included in this bibliography. Thelist is arranged in alphabetical order by the nameof the journal.
ACM SIGAPL APL Quote Quad, [32]
Appl. Math. Comput. (USA), [145]
Autom. Constr., [26]
Axis (UK), [88]
Build Environment, [27]
Communications of the ACM, [150]Complexity (USA), [29]
Complexity International, [172]
Computer Graphics, [49]
Computer Music Journal, [156]
Computer Physics Communications, [147]
Computer-Aided Design, [36]
Computers in Chemical Engineering, [142]
Connect. Science, [39]
Digit. Creat. (UK), [137]
Engineering Applications of Artificial Intelligence, [31]
Ethology and Sociobiology, [95]
Forma, [111]Graphical Models, [98]
Helsingin Sanomat, [59]
IBM asiaa, [61]
IBM Journal of Research & Development, [99]
IBM Systems Journal, [45]
IEEE Transaction on Visualization and Computer Graph-ics, [103]
IEEE Transactions on Circuits and Systems for Video Tech-nology, [34]
IEEE Transactions on Speech & Audio Processing, [75]
IEEE Transactions on Speech and Audio Processing, [64,
65, 67, 76]
IEEE Transactions on Systems, Man, and Cybernetics,[108]
IEEE Transactions on Systems, Man, and Cybernetics-PartC: Applications and Reviews, [32]
INFORMS J. Comput., [141]
Internet Today, [129]
J. New Music Res. (Netherlands), [40]
Journal of Audio Engineers Society, [63]
Journal of Computers and Graphics, [116]
Journal of the Audio Engineering Society, [68, 69, 70, 71,
72, 73, 74]
Journal of Visualization and Computer Animation, [56, 82]
Kybernetes, [162]
Laryngoscope, [15]
Lighting Research and Technology, [23, 24]
Microcomputers in Civil Engineering, [16]
Muhely (The Hungarian Journal of Modern Art), [62]
New Scientist, [46]
Pattern Recognition, [97]
Scandinavian Audiology, [66]
Scientific Computing World, [139]
9
8/10/2019 Bibliography of Genetic Algorithms in Arts and Music
http://slidepdf.com/reader/full/bibliography-of-genetic-algorithms-in-arts-and-music 14/49
10 Genetic algorithms in arts and music
SIGCSE Bulletin, [90]
The Journal of the Acoustical Society of America, [176, 177]
The Visual Computer, [52, 127]
Transactions of the Institute of Electronics, Information,and Communication Engineers D-II (Japan), [87,
146]
total 63 articles in 49 series
4.3 Theses
The following two lists contain theses, first PhDtheses and then Master’s etc. theses, arranged inalphabetical order by the name of the school.
4.3.1 PhD theses
Columbia University, [161]
Eotvos Lorand University, [104]
Harvard University, [77]
University of Erlanger-Nurnberg, [117]
University of Michigan, [67]
University of Surrey, [180]
total 6 thesis in 6 schools
4.3.2 Master’s theses
This list includes also “Diplomarbeit”, “Tech. Lic.
Theses”, etc.
Technische Hochschule Darmstadt, [110]
University of Industrial Arts Helsinki, [30]
total 2 thesis in 2 schools
4.4 Report series
The following list contains references to all pa-pers published as technical reports. The list isarranged in alphabetical order by the name of the
institute.
IBM, [54]
Max-Planck Intitut fur Informatik, [115]
University of Illinois at Urbana-Champaign, [153]
University of Vaasa, [43]
total 4 reports in 4 institutes
4.5 Patents
The following list contains the names of thepatents of genetic algorithms in arts and music.The list is arranged in alphabetical order by thename of the patent.
Data structure for system kitchen editing and designing,[20]
House design system using genetic algorithm, [21]
House design system using genetic algorithm, [18, 19]
Method and device for generating musical sound waveform,[184]
total 4 patents
8/10/2019 Bibliography of Genetic Algorithms in Arts and Music
http://slidepdf.com/reader/full/bibliography-of-genetic-algorithms-in-arts-and-music 15/49
Authors 11
4.6 Authors
The following list contains all genetic algorithms in arts and music authors and references to their knowncontributions.
Agah, Arvin, [32]
Agui, Takeshi, [87]
Ahn, Byung-Ha, [159]
Alander, Jarmo T., [1, 43]
Ala-Siuru, Pekka, [33]
Alfonseca, Manuel, [99]
Alfonso, Rafael Sanhez, [158, 160]
Anderson, Peter G., [175]
Angeline, Peter J., [132]
Aoki, A., [146]
Aoki, K., [125, 138]
Auramo, Yrjo, [66]
Ayers, L., [170, 176]
Bair, Alethea, [103]
Baker, Ellie, [38]
Baluja, Shumeet, [39]
Baum, Karl G., [105]
Beauchamp, James, [63, 156]
Bersano-Begey, Tommaso F., [178]
Beyer, Markus, [109, 110,112, 115]
Biles, J. A., [175]
Biles, John A., [167, 177]
Birchfield, David Andrew, [161]
Blanchet, C., [24]
Boccara, M., [84]
Branke, Jurgen, [17]
Brice, A. A., [142]
Bucher, Frank, [17]
Budiarto, Rahmat, [111]
Burton, A. R., [179]
Burton, Anthony Richard, [180]
Bustillo, Eduardo, [136]
Buxton, Bernard F., [80]
Byrne, J. A., [157]
Caldalda, J. J. Romero, [185]
Chan, San-Kuen, [72]
Chang, Seok C., [159]
Chen, Wen-Pin, [15]
Chen, Yeong-Chinq, [97]
Chetverikov, Dmitry, [101, 102]
Cheung, N.-M., [71]
Cho, Sung-Bae, [31]
Chou, Jyh-Rong, [36]
Clark, Sean, [129]
Coley, D. A., [27, 29]
Corcione, M., [23]
Cotta, Carlos, [106]
Crabb, J. A., [27]
Crochemore, D., [84, 86]
Daida, Jason M., [178]
Dainghaus, R., [122]
Dalhoum, Abdellatif Abu, [99]
de Vega, Francisco Fernandez, [163,164]
Dequn, Liang, [131]
Devcic, Zlatko, [15]
Ding, Lan, [28]
Dodgson, N., [92]
Dorado, J., [185]
dos Reis, Gustavo Miguel Jorge, [163,164]
Durant, E. A., [67]
Durant, Eric A., [67]
Eastman, Charles M., [26]
Ebner, Marc, [100]
Ekart, Aniko, [102]
Fernandez, Francisco, [165]
Fernandez de Vega, F., [106]
Ferreira, Aniıbal, [165]
Flaig, T., [122]
Fontana, L., [23]
Fovargue, A., [90]
Frade, Miguel, [106]
Franklin, M., [95]
Fujimura, Kikou, [127]
Fujimura, N., [125]
Fukunaga, Alex, [81]
Furuta, H., [16, 120]
Garigliano, Roberto, [116]
Geigel, Joe, [78]
Gero, John S., [28]
Gervautz, M., [130]
Gibson, P. M., [157]
Goldberg, David E., [152, 153, 154]
Graf, Jeanine, [119]
Greenwood, Garrison W., [75]
Griffiths, S., [88]
Gritz, Larry, [82, 89, 78]
Gustafson, Steven C., [148]
Hahn, James K., [82, 89]
Hahn, James, [78]
Haken, Lippold, [63, 156]
Hall, M. A., [171]
Hamer, R. D., [88]
Hamid, Mahmoud S., [34, 35]
Harvey, Neal R., [34]
Helguera, Marıa, [105]
Herdy, Michael, [42]
Hirose, A., [120]
Ho, Shinn-Ying, [97]
Hobden, A., [123]
Homaifar, Abdollah, [155]
Horner, Andrew B., [170, 40, 68,71, 72, 73, 176, 74, 152, 153, 154,63, 156]
Horner, Andrew, [64, 76]
Horowitz, Damon, [166]
House, Donald H., [103]
Hughes, P., [56]
Hung, Chia-Young, [37]
Hung, Fei-Kung, [37]
Hwu, Jiing-Yuan, [65]
Iba, Hitoshi, [79]
Itoh, Hidenori, [111]
Jackson, D., [90]
8/10/2019 Bibliography of Genetic Algorithms in Arts and Music
http://slidepdf.com/reader/full/bibliography-of-genetic-algorithms-in-arts-and-music 16/49
8/10/2019 Bibliography of Genetic Algorithms in Arts and Music
http://slidepdf.com/reader/full/bibliography-of-genetic-algorithms-in-arts-and-music 17/49
Authors 13
Traxler, C., [130]
Tsai, Hung-Cheng, [36, 37]
Tsutsumi, Kazutoshi, [25]
Tuson, Andrew, [181]
Veitch, J. A., [22, 24]
Ventrella, J., [41]
Vepsalainen, Marja-Leena, [61]
Verner, O. V., [141]
Viikki, Kati, [66]
Vladimirova, T., [179]
Vucic, Vedran, [62]
Wainwright, R. L., [141]
Wakaki, Hiromi, [79]
Wakefield, Gregory H., [67]
Ware, Colin, [103]
Watanabe, E., [16]
Wiggins, Geraint, [181, 182]
Williams, R. D., [88]
Winters, D., [29]
Wong, Brian J. F., [15]
Wong, Kit Po, [91]
Xuan, Yang, [131]
Yamada, Masashi, [111]
Yamamoto, Kenji, [140]
Yang, H. C., [151]
Yang, Yee-Hong, [98]
Yoshida, Toshiya, [184]
Yu, J. F., [151]
Yuen, J., [74]
Zhang, Y. G., [145]
total 172 articles by 264 differ-ent authors
8/10/2019 Bibliography of Genetic Algorithms in Arts and Music
http://slidepdf.com/reader/full/bibliography-of-genetic-algorithms-in-arts-and-music 18/49
14 Genetic algorithms in arts and music
4.7 Subject index
All subject keywords of the papers given by the editor of this bibliography are shown next.
acoustics, [176]
double frequency, [69]
drum, [172]
aesthetics, [17, 29]
bridge design, [16]
agents
communications, [162]
ecosystems, [162]
analysing GA
animation, [90]
animation, [47, 77, 78,81, 82, 83, 84, 85, 41, 87, 91]
animation
3D characters, [89]
CAL, [88]
cloth, [86]
computer graphics, [92]
application
computer graphics, [52, 53]
architecture, [27, 29, 18,19, 20, 21, 25]
aesthetics, [28]
CAD, [26]
illumination design, [22, 23, 24]
art, [44, 45, 46,48, 54, 49, 55, 56, 50, 51, 57, 47,52, 53, 59, 58, 60, 61, 62]
art
artificial life, [30]
bibliography, [43]
cartoons, [41]
color design, [36, 37]
colours, [42]
computer generated, [38, 39]
digital, [32]
fashion, [31]
movies, [34, 35]
music, [40]
review, [33]
artificial life, [59, 129]
trees, [145]
audiosynthesis, [74]
Avatar, [79]
bibliography
art, [43]
music, [43]
special, [43]
CAD, [108, 119, 101]
buildings, [26]
color design, [36, 37]
face, [15]
CD-ROM
graphics, [58]
chemistry
structural, [77]
chromosomes
polyploid, [96, 135]
classifier systems, [92]
clothes
design, [31]
color
combinations, [137]
color harmony, [36, 37]
colors
visualization, [105]
computational geometry, [100]
shape modeling, [128]
computer games
graphics, [106]
computer graphics, [44,45, 46, 48, 54, 49, 55, 56, 94, 50,57, 93, 47, 107, 108, 109, 111, 113,114, 58, 83, 117, 60, 119, 125, 127,132, 134, 17, 136, 139, 62, 147, 148,150, 151, 96, 97, 135, 32, 104]
computer graphics
animation, [79, 81]
art, [129]
color, [137, 105]
face, [95]
flowsheet drawing, [142]fractals, [116, 130, 131]
GP, [1]
graphs, [121, 143]
L-systems, [124, 149, 99]
labeling, [141]
lighting, [146]
Lindenmayer systems, [144]
photorealism, [101]
photorealistic, [102]
ray trace, [118]
ray tracing, [110, 112, 115]
redering, [122]
rendering, [110, 112, 115]
scene graphs, [100]
shading, [128]
stereo, [103]
superquadrics, [133, 140]
texture, [123]
texture generation, [98]
trees, [145]
computer graphics?, [120, 126, 138]control, [67]
motion, [82, 87]
design, [120]
fashion, [31]
display, [111]
dress
design, [31]
engineering
civil, [26, 16, 27, 25]
structural, [16]
esthetics
KANSEI, [25]
evolution
beauty, [95]
simulation, [117]
evolution strategies, [75, 96, 135]
interactive, [42]
Evolvica, [96, 135]
8/10/2019 Bibliography of Genetic Algorithms in Arts and Music
http://slidepdf.com/reader/full/bibliography-of-genetic-algorithms-in-arts-and-music 19/49
Subject index 15
expert systems, [66]
faces, [83]
FEM, [25]
acoustics, [172]
films
restoration, [34, 35]
filter
morphological, [34, 35]
fitness
aesthetics, [15]
interactive, [15]
fitness function
human, [95]
interactive, [31]
neural network, [175, 179]
fractals, [116, 130]
IFS, [131, 145]
L-systems, [99]
fuzzy systems, [122]
GALAPAGOS, [94]
genetic programming, [49, 50, 51,52, 53, 82, 123, 126, 89, 178, 147,96, 135]
genetic programming
computer graphics, [106, 1]
L-systems, [107]
music, [173]
geometry
polygonal approximation, [97]
grammars
music, [158, 160]
graphs
drawing, [121]
layout, [114]
rendering, [143]
rrawing, [17]
hearing aid, [67]
hybrid
neural networks, [180]
simulated annealing, [70]
iamging
stereo, [103]
illumination
design, [22, 23, 24]
image processing
color, [137]
fractals, [131]
image registration, [101, 102]
range image, [140]
registration, [104]
restoration, [34, 35]
shading, [133]
shape, [128]
synthetic, [32]
image registration
3D, [101, 102]
implementation
Connection Machine, [49]
Mathematica, [124, 96, 135]
industrial art, [31]
interactive, [94]
interactive design, [125]
interactive GA, [119]
Internet, [129]
inverse problems
fractals, [131]
L-System, [107]
L-systems, [93, 117, 126,134, 134, 147]
Latham, [59, 129]
layout design, [94, 108]
flowsheet, [142]
labels in maps, [141]
lighting, [125]
Lindenmayer systems, [96, 135]
machine learning, [114, 28]
mathematics
integration, [118]
medical imaging
visualization, [105]
medicine
neurology, [66]
plastic surgery, [15]
sensing, [67]
modulation
double frequency, [69]
music, [168, 169,170, 40, 69, 173, 175, 176, 180]
adaptive synthesis, [183]
analysis, [171]
bibliography, [43]
composing, [178]
composition, [155, 174,179, 181, 185, 158, 160, 161]
drum, [172]
electronic, [70, 74]
instruments, [68]
jazz, [167, 177]
jazz melodies, [182]
rhytms, [166]
synthesis, [163, 164]
tones, [63, 156, 72,72]
transcription, [165]
wavetables, [73]
music composition, [152, 153,154, 157]
music?, [162]
neural networks, [157, 87, 136]
fitness, [179]
training, [75]
optics
lighting design, [23]
ray tracing, [109]
optimization
global, [77]
parallel GA, [49]
parameter estimation
texture, [98]
patent, [184, 18, 19,20, 21]
pattern recognition, [65]
music, [159]
shape representation, [97]
physics
mechanics, [41]
placing, [141]
plants
artificial, [96, 135]
popular, [61, 129]
8/10/2019 Bibliography of Genetic Algorithms in Arts and Music
http://slidepdf.com/reader/full/bibliography-of-genetic-algorithms-in-arts-and-music 20/49
16 Genetic algorithms in arts and music
neural networks, [139]
rendering
sound, [78]
review
AI in art, [33]
artificial life, [30]
selection
interactive, [42]
sensing
hearing, [67]
sexual selection, [95]
shape design
roofs, [25]
wind turbine, [100]
signal processing, [68, 76, 64]
audio, [71, 73, 176]
modulation, [70]
music, [184, 163, 164]
sampling, [74]
speech, [65]
tones, [72]
sound, [162]
composition, [78]
superquadrics, [133, 140]
tomography
MR, [105]
PET, [105]
tutorial
animation, [80]
video tape, [48]
virtual reality, [122]
visualisation, [148]
visualization
palette design, [105]
8/10/2019 Bibliography of Genetic Algorithms in Arts and Music
http://slidepdf.com/reader/full/bibliography-of-genetic-algorithms-in-arts-and-music 21/49
Annual index 17
4.8 Annual index
The following table gives references to the contributions by the year of publishing.
1989, [44, 45]
1990, [46, 48, 54]1991, [152, 153, 154, 157, 49, 55, 56]
1992, [94, 50, 51, 57]
1993, [155, 63, 156, 93, 47, 77, 52, 53, 78, 95]
1994, [38, 81, 166, 107, 167, 108, 168, 109,110, 169, 111, 59, 39, 112, 113, 114, 115]
1995, [58, 170, 116, 26, 82, 83, 40, 117, 118,84, 60, 85, 41, 61, 16, 119, 120, 68, 86, 171, 87, 121, 122,88, 123, 124, 172, 43]
1996, [125, 69, 70, 42, 126, 127, 173, 128,129, 174, 130, 131, 132, 175, 133, 134, 71, 72, 73, 176]
1997, [17, 27, 136, 89, 137, 177, 90, 28, 74,138, 139, 178, 140, 141, 62, 179, 29, 75]
1998, [142, 76, 180, 143, 144, 181, 182, 183,184, 145, 146]
1999, [91, 185, 147, 92, 148, 149, 150, 151]
2000, [64, 65, 18, 30, 31, 19]
2001, [96, 66, 20, 97, 21, 135]
2002, [158, 67, 67, 22, 79, 32, 33, 159, 98, 160]
2003, [99, 80, 161, 162, 100, 23, 34, 35]
2005, [24, 101]
2006, [102, 103, 25, 104]
2007, [163, 36, 164, 37]
2008, [15, 105, 106]
2010, [1]
2012, [165]
8/10/2019 Bibliography of Genetic Algorithms in Arts and Music
http://slidepdf.com/reader/full/bibliography-of-genetic-algorithms-in-arts-and-music 22/49
18 Genetic algorithms in arts and music
4.9 Geographical index
The following table gives references to the contributions by country.
• Australia: [28, 9 1, 162]
• Austria: [130]
• Canada: [150, 9 6, 2 2, 9 8, 2 4]
• China: [131, 145, 151, 40, 71, 72, 73, 176, 74, 76, 64]
• Cuba: [121, 143]
• Denmark: [62]
• Finland: [78, 168, 59 , 60, 85, 61, 43, 183, 30 , 66, 33, 1]
• France: [86, 172]
• Germany: [93, 109, 110, 112, 115, 117, 118, 119, 122,124, 42, 134, 17, 147, 135, 100]
• Hungary: [101, 102, 104]
• Italy: [23]
• Japan: [79, 114, 16, 120, 68, 87, 125, 128, 133, 138, 140,184, 146, 18, 19, 20, 21, 25]
• New Zealand: [171]
• Singapore: [70]
• South Korea: [83, 3 1, 159]
• Spain: [136, 185, 158, 160, 99, 163, 164, 106, 165]
• Taiwan: [65, 97, 36, 37]
• United Kingdom: [44, 45, 46, 54, 157, 55, 56, 57, 47,116, 88, 123, 129, 27, 137, 90, 139, 179, 29, 142, 180,144, 181, 182, 92 , 80]
• United States: [48, 152, 153, 154, 49, 50, 51, 155, 63,156, 77, 52, 53, 95, 38, 81, 166, 167, 169, 39, 113, 58,26, 82, 41, 127, 173, 175, 177, 178, 141, 75, 148, 149,67, 3 2, 161, 34, 3 5, 103, 105]
• Unknown country: [111, 170, 6 9, 174, 89, 1 5]
8/10/2019 Bibliography of Genetic Algorithms in Arts and Music
http://slidepdf.com/reader/full/bibliography-of-genetic-algorithms-in-arts-and-music 23/49
Chapter 5
Permuted title index
The words of the titles of the articles are shown in the next table arranged in alphabetical order. Themost common words have been excluded. The key word is shown by a disk (•) in the title field with theexception that it is omitted when appearing as the first word of the title after shown keyword. The otherabbreviation used to compress titles are shown in appendix A.
[97] accurate An efficient evol. alg. for • polygonal approx-imation
[93] activity Patterns of cluster formation and evol. • inevolving L-syst.
[176] adaptive Common tone • tuning using GAs[183] additive Opt. • synthesis parameters with GAs and
self-organizing maps[16] aesthetic Appl. of GA to • design of bridge structures[29] – GA search efficacy in • product spaces[15] aesthetics Evolving attractive faces using morphing
technology and a GA: A new appr. to determining ideal facial•
[33] AI in contemporary (interactive)art[67] Aid Hearing • Fitting with GAs[105] algorithm-generated Evaluation of gen. • multivari-
ate color tables for the visualization of multimodal medicalfused data sets
[60] Alkavatko taideteoksetkin elaa? [Is art getting life?][64] amplitudes Low peak • for wavetable synthesis[81] animated Automatic cntr. of physically realistic • fig-
ures using EP[41] – Disney meets Darwin – the evol. of funny • figures[78] – Using physically-based models and GAs for functional
composition of sound signals, synchronized to • motion[91] Animating the evol. process of GAs[84] animation Building new tools for synthetic image • by
using evol. techniques[56] – Computer sculpture design and •
[80] – Evol. alg. in modeling and •
[86] – Evol. ident. of cloth • models[89] – Gen. prog. evol. of cntr. for 3-D character •
[77] – Global Opt. for Articulated Figures: Molecular Struc-ture Prediction and Motion Synthesis for •
[79] – Motion design of a 3D-CG avatar that uses humanoid•
[47] – The appl. of evol. and biological processes to computer
art and •
[90] – The use of • to explain GAs[88] – Three-dimensional colour image and • modelling for
CAL[70] annealing Automated parameter opt. for douple fre-
quency modulation synthesis using the gen. • alg.[69] – Automatic parameter opt. for double frequency mod-
ulation synthesis using the gen. • alg.[174] application GeNotator: An environment for investigat-
ing the • of GAs in computer assisted composition[156] – Machine tongues XVI. GAs and their • to FM matching
synthesis[16] • of GA to aesthetic design of bridge structures[120] • of GA to design of artificial ground
[31] • of interactive GA to fashion design[34] applications GA opt. of multidimensional grayscale
soft morphological filters with • in film archive restoration[47] – The • of evol. and biological processes to computer art
and animation[173] applied A grammar based gen. prog. technique • to
music generation[180] – A hybrid neuro-gen. pattern evol. syst. • to musical
composition[1] Applying GArphics - • GAs for generating graphics[110] Approximation der Rendering Equation durch Evol.
are Alg. en[67] approximations Efficient model fitting using a GA:
pole-zero • of HRTFs[34] archive GA opt. of multidimensional grayscale soft
morphological filters with appl. in film • restoration[60] art Alkavatko taideteoksetkin elaa? [Is • getting life?]
[55] – Artificial life or surreal •[57] – Evol. • and Computers[43] – Indexed Bibliography of GAs in • and Music[129] – Organic •
[47] – The appl. of evol. and biological processes to computer• and animation
[61] – Tietokonetaide on monien ilmioiden leikkauspiste[Computer •
[159] ART-1 Music recognition syst. using • and GA[82] articulated Gen. prog. for • figure motion[77] – Global Opt. for • Figures: Molecular Structure Pre-
diction and Motion Synthesis for Animation[120] artificial Appl. of GA to design of • ground[30] – Geneesys – katsaus kolmiulotteiseen keinoelamaan nyt
ja hahmotelma tulevaisuudesta [A Review of 3D • Life andOutline of its Future]
[124] – Gen. L-syst. prog. : breeding and evolving • flowerswith Mathematica
[145] – Lifelike • trees based on growth iterated function syst.
[49] • evol. for computer graphics[39] – Towards automated • evol. for computer-generated im-
ages[27] Artificial intelligence appr. to the prediction of nat.
lighting levels[85] Artificial life Keinoelamaa virtuaalitodellisuudessa –
hyttysia ja muita otokoita • in virtual reality – Gnats andother little creatures]
[150] Artificial life for computer graphics[55] • or surreal art?[59] artist Kaarmemaiset sykkyrat pyorivat, hajoavat ja
kulkevat itsensa lapi, Tietokonetaiteilija William Latham luoolioitaan evoluution saantojen avulla [Refers to works of com-puter • William Latham]
19
8/10/2019 Bibliography of Genetic Algorithms in Arts and Music
http://slidepdf.com/reader/full/bibliography-of-genetic-algorithms-in-arts-and-music 24/49
20 Genetic algorithms in arts and music
[62] arts Self-evolving • Organisms versus fetishes[174] assisted GeNotator: An environment for investigating
the appl. of GAs in computer • composition[36] association Automatic design support and image eval-
uation of two-coloured products using colour • and colour har-mony scales and GA
[15] attractive Evolving • faces using morphing technologyand a GA: A new appr. to determining ideal facial aesthetics
[171] attributed Sel. of • for modeling Bach chorales by aGA
[70] Automated parameter opt. for douple frequency mod-ulation synthesis using the gen. annealing alg.
[39] – Towards • artificial evol. for computer-generated im-ages
[163] automatic A novel appr. to • music transcription usingelectronic synthesis and GAs
[87] – An • GA-based construction of neural networks for mo-tion cntr. of virtual life
[164] – Electronic synthesis using GAs for • music transcrip-tion
[165] – Evol. alg. and • transcription of music[158, 160] • composition of music by means of grammatical
evol.[81] • cntr. of physically realistic animated figures using EP[36] • design support and image evaluation of two-coloured
products using colour association and colour harmony scalesand GA
[121] • graph drawing by gen. search[69] • parameter opt. for double frequency modulation syn-
thesis using the gen. annealing alg.[37] • product color design using gen. searching[108] Automating the layout of network diagrams with spec-
ified visual organization[79] avatar Motion design of a 3D-CG • that uses humanoid
animation[59] avulla Kaarmemaiset sykkyrat pyorivat, hajoavat ja
kulkevat itsensa lapi, Tietokonetaiteilija William Latham luoolioitaan evoluution saantojen • [Refers to works of computerartist William Latham]
[169] Bach in a box: The evol. of four part baroque harmonyusing the GA
[171] Bach Sel. of attributed for modeling • chorales by a GA[169] baroque Bach in a box: The evol. of four part • har-
mony using the GA[95] beauty Is • in the eye of the beholder[95] beholder Is beauty in the eye of the •
[43] Bibliography Indexed • of GAs in Art and Music[47] biological The appl. of evol. and • processes to com-
puter art and animation[92] blending Motion • using a CS[169] box Bach in a • The evol. of four part baroque harmony
using the GA[124] breeding Gen. L-syst. prog. : • and evolving artificial
flowers with Mathematica[16] bridge Appl. of GA to aesthetic design of • structures[25] buildings Roof shape generation method for • using
KANSEI evaluation rules[88] CAL Three-dimensional colour image and animation
modelling for •
[111] cat’s A • cradle string diagram display method based ona GA
[65] cepstrum GA-based noisy speech recognition usingtwo-dimensional •
[138] CG 3-D • lighting with an interactive GA[89] character Gen. prog. evol. of cntr. for 3-D • anima-
tion[171] chorales Sel. of attributed for modeling Bach • by a
GA[92] classifier Motion blending using a • syst.[86] cloth Evol. ident. of • animation models[93] cluster Patterns of • formation and evol. activity in
evolving L-syst.[147] coevolution Evol. and • of developmental prog.[37] color design Automatic product • using gen. searching[105] color tables Evaluation of GA-generated multivariate
• for the visualization of multimodal medical fused data sets[137] colour A two-stage evol. model for the computer-aided
design of • combinations[36] – Automatic design support and image evaluation of two-
coloured products using • association and colour harmonyscales and GA
[36] colour harmony Automatic design support and imageevaluation of two-coloured products using colour associationand • scales and GA
[88] colour image Three-dimensional • and animation mod-elling for CAL
[137] combinations A two-stage evol. model for thecomputer-aided design of colour •
[103] complex An appr. to the perceptual opt. of • visual-izations
[44] – FormSynth: The rule-based evol. of • forms from geo-metric primitives
[155] composes GA • music[178] composing Musica ex machina: • 16th-century coun-
terpoint with gen. prog. and symbiosis[180] composition A hybrid neuro-gen. pattern evol. syst.
appl. to musical •[158, 160] – Automatic • of music by means of grammatical
evol.[181] – Evol. methods for musical •[179] – GA utilising neural network fitness evaluation for mu-
sical •[152, 153, 154] – GAs and computer-assisted music •
[174] – GeNotator: An environment for investigating the appl.of GAs in computer assisted •
[157] – Neurogen, music • using GAs and cooperating neuralnetworks
[78] – Using physically-based models and GAs for functional• of sound signals, synchronized to animated motion
[185] compositor Gen. music •
[96] Computation Illustrating Evol. • with Mathematica[73] • and memory tradeoffs with multiple wavetable inter-
polation[137] computer-aided A two-stage evol. model for the • de-
sign of colour combinations[152, 153, 154] computer-assisted GAs and • music compo-
sition[39] computer-generated Towards automated artificial
evol. for • images[151] connection Optimized • of rational surface-based on
GAs[114] constraints Evol. learning of graph layout • from ex-
amples[87] construction An automatic GA-based • of neural net-
works for motion cntr. of virtual life[33] contemporary AI in • (interactive)art[87] control An automatic GA-based construction of neural
networks for motion • of virtual life[81] – Automatic • of physically realistic animated figures us-
ing EP[122] controlled A fuzzy • rendering syst. for virtual reality
syst. optimised by GAs[89] controllers Gen. prog. evol. of • for 3-D character
animation[157] cooperating Neurogen, music composition using GAs
and • neural networks[178] counterpoint Musica ex machina: composing 16th-
century • with gen. prog. and symbiosis[111] cradle A cat’s • string diagram display method based
on a GA[102] Creating photorealistic models by data fusion with GAs[113] creatures Evolving virtual •[85] – Keinoelamaa virtuaalitodellisuudessa – hyttysia ja
muita otokoita [Artificial life in virtual reality – Gnats andother little •
[130] CSG-PL-systems Using GAs to improve the visualquality of fractal plants generated with •
[99] curves Grammatical evol. to design fractal • with a
given dimension[41] Darwin Disney meets • – the evol. of funny animated
figures[58] delights In the infinity of computer space there is a gar-
den of unearthly •
[137] design A two-stage evol. model for the computer-aided• of colour combinations
[16] – Appl. of GA to aesthetic • of bridge structures[120] – Appl. of GA to • of artificial ground[31] – Appl. of interactive GA to fashion •
[36] – Automatic • support and image evaluation of two-coloured products using colour association and colour harmonyscales and GA
[56] – Computer sculpture • and animation
8/10/2019 Bibliography of Genetic Algorithms in Arts and Music
http://slidepdf.com/reader/full/bibliography-of-genetic-algorithms-in-arts-and-music 25/49
Permuted title index 21
[100] – Evol. • of objects using scene graphs[35] – FPGA realisation of the GA for the • of gray-scale soft
morphological filters[99] – Grammatical evol. to • fractal curves with a given di-
mension[21] – House • syst. using GA[119] – Interactive evol. alg. in •
[146] – Interactive GA-based • support syst. for lighting • in3-D computer graphics
[125] – Interactive GA-based • support syst. for lighting • incomputer graphics
[79] – Motion • of a 3D-CG avatar that uses humanoid ani-mation
[23] – Opt. • of outdoor lighting syst. by GAs[20] designing Data structure for syst. kitchen editing and
•
[15] determining Evolving attractive faces using morphingtechnology and a GA: A new appr. to • ideal facial aesthetics
[147] developmental Evol. and coevol. of • prog.[184] device Method and • for generating musical sound
waveform[111] diagram A cat’s cradle string • display method based
on a GA[108] diagrams Automating the layout of network • with
specified visual organization[141] – Placing text labels on maps and • using GAs with
masking[32] digital Evol. of • images[99] dimension Grammatical evol. to design fractal curves
with a given •
[72] Discrete summation synthesis of musical instrumenttones using GAs
[41] Disney meets Darwin – the evol. of funny animated fig-ures
[111] display A cat’s cradle string diagram • method basedon a GA
[70] douple Automated parameter opt. for • frequency mod-ulation synthesis using the gen. annealing alg.
[17] drawing A GA for • undirected graphs[121] – Automatic graph • by gen. search[143] – Gen. graph •
[142] – Opt. of flowsheet • layout using a GA[172] drum A • shape opt. by GAs[68] dynamic Wavetable matching synthesis of • instru-
ments with GAs[50] dynamical Interactive evol. of • syst.[162] ecosystems Evolving sonic •
[20] editing Data structure for syst. kitchen • and designing[29] efficacy GA search • in aesthetic product spaces[97] efficient An • evol. alg. for accurate polygonal approx-
imation[67] • model fitting using a GA: pole-zero approximations of
HRTFs[60] elaa Alkavatko taideteoksetkin • [Is art getting life?][163] electronic A novel appr. to automatic music transcrip-
tion using • synthesis and GAs[164] • synthesis using GAs for automatic music transcription[28] emergent Learning • style using an evol. appr.[117] Entwicklungsprogrammen MathEvolvica –
Simulierte Evol. von • der Natur[40] Envelope matching with GAs[174] environment GeNotator: An • for investigating the
appl. of GAs in computer assisted composition[110] Equation Approximation der Rendering • durch Evol.
are Alg. en[51, 52] equations Interactive evol. of • for procedural models[98] Estimating parameters for procedural texturing by
GAs[36] evaluation Automatic design support and image • of
two-coloured products using colour association and colour har-mony scales and GA
[179] – GA utilising neural network fitness • for musical com-position
[105] • of GA-generated multivariate color tables for the vi-sualization of multimodal medical fused data sets
[25] – Roof shape generation method for buildings usingKANSEI • rules
[180] evolution A hybrid neuro-gen. pattern • syst. appl. tomusical composition
[118] – An • model for integration problems[91] – Animating the • process of GAs
[49] – Artificial • for computer graphics[158, 160] – Automatic composition of music by means of gram-
matical •[169] – Bach in a box: The • of four part baroque harmony
using the GA[41] – Disney meets Darwin – the • of funny animated figures[117] – MathEvolvica – Simulierte • von Entwicklungsprog.
men der Natur[135] – Principia Evolvica, Simulierte • mit Mathematica[126] – Evolving • prog. : Gen. prog. and L-syst.[44] – FormSynth: The rule-based • of complex forms from
geometric primitives[89] – Gen. prog. • of cntr. for 3-D character animation[99] – Grammatical • to design fractal curves with a given
dimension[50] – Interactive • of dynamical syst.[51, 52] – Interactive • of equations for procedural models[54] – Mutator, a subjective human interface for • of com-
puter sculptures[147] • and coevol. of developmental prog.[32] • of digital images[134] • prog. evolved[22] – Preferred surface luminances in offices, by • a pilot
study[39] – Towards automated artificial • for computer-generated
images[42] Evolution strategies with subjective sel.[110] Evolutionare Approximation der Rendering Equation
durch • Alg. en[137] evolutionary A two-stage • model for the computer-
aided design of colour combinations[97] – An efficient • alg. for accurate polygonal approxima-
tion[81] – Automatic cntr. of physically realistic animated figures
using • prog.[84] – Building new tools for synthetic image animation by
using • techniques[96] – Illustrating • Computation with Mathematica[119] – Interactive • alg. in design[28] – Learning emergent style using an • appr.[165] • alg. and automatic transcription of music[80] • alg. in modeling and animation[57] • Art and Computers[148] • data visualization[100] • design of objects using scene graphs[86] • ident. of cloth animation models[114] • learning of graph layout constraints from examples[181] • methods for musical composition[93] – Patterns of cluster formation and • activity in evolving
L-syst.[109] – Rayvolution: an • ray tracing alg.[112] – Rayvolution: An • ray tracing alg.[115] – Simulation of Global Illumination: An • Appr.[47] – The appl. of • and biological processes to computer art
and animation[75] evolutionary strategies Training partially recurrent
neural networks using •
[59] evoluution Kaarmemaiset sykkyrat pyorivat, hajoavat ja kulkevat itsensa lapi, Tietokonetaiteilija William Lathamluo olioitaan • saantojen avulla [Refers to works of computerartist William Latham]
[134] evolved Evol. prog. •
[135] Evolvica Principia • Simulierte Evol. mit Mathemat-
ica
[124] evolving Gen. L-syst. prog. : breeding and • artificialflowers with Mathematica
[15] • attractive faces using morphing technology and a GA:
A new appr. to determining ideal facial aesthetics[126] • evol. prog. : Gen. prog. and L-syst.[132] • fractal movies[116] • fractals[53] • images[161] • intelligent musical materials[38] • line drawings[162] • sonic ecosyst.[113] • virtual creatures[93] – Patterns of cluster formation and evol. activity in •
L-syst.[178] ex Musica • machina: composing 16th-century counter-
point with gen. prog. and symbiosis[66] Experiances of otoneurological expert syst. for vertigo
8/10/2019 Bibliography of Genetic Algorithms in Arts and Music
http://slidepdf.com/reader/full/bibliography-of-genetic-algorithms-in-arts-and-music 26/49
22 Genetic algorithms in arts and music
[106] experience Modelling video games’ landscapes bymeans of gen. terrain prog. - A new appr. for improvingusers’ •
[66] expert Experiances of otoneurological • syst. for ver-tigo
[90] explain The use of animation to • GAs[83] expression Searching for facial • by GA[95] eye Is beauty in the • of the beholder[15] faces Evolving attractive • using morphing technology
and a GA: A new appr. to determining ideal facial aesthetics[15] facial Evolving attractive faces using morphing technol-
ogy and a GA: A new appr. to determining ideal • aesthetics[83] – Searching for • expression by GA[31] fashion Appl. of interactive GA to • design[76] feedback Nested modulator and • FM matching of in-
strument tones[62] fetishes Self-evolving arts— Organisms versus •
[82] figure Gen. prog. for articulated • motion[81] figures Automatic cntr. of physically realistic animated
• using EP[41] – Disney meets Darwin – the evol. of funny animated •
[77] – Global Opt. for Articulated • Molecular Structure Pre-diction and Motion Synthesis for Animation
[34] film GA opt. of multidimensional grayscale soft morpho-logical filters with appl. in • archive restoration
[35] filters FPGA realisation of the GA for the design of gray-scale soft morphological •
[34] – GA opt. of multidimensional grayscale soft morpholog-ical • with appl. in film archive restoration
[139] fish Where to • for neural nets[179] fitness GA utilising neural network • evaluation for mu-
sical composition[175] fitness functions Neural network • for a musical IGA[67] fitting Efficient model • using a GA: pole-zero approxi-
mations of HRTFs[67] – Hearing Aid • with GAs[124] flowers Gen. L-syst. prog. : breeding and evolving
artificial • with Mathematica[142] flowsheet Opt. of • drawing layout using a GA[156] FM Machine tongues XVI. GAs and their appl. to •
matching synthesis[76] – Nested modulator and feedback • matching of instru-
ment tones[93] formation Patterns of cluster • and evol. activity in
evolving L-syst.[44] forms FormSynth: The rule-based evol. of complex •
from geometric primitives[44] FormSynth The rule-based evol. of complex forms
from geometric primitives[35] FPGA realisation of the GA for the design of gray-scale
soft morphological filters[132] fractal Evolving • movies[99] – Grammatical evol. to design • curves with a given di-
mension[130] – Using GAs to improve the visual quality of • plants
generated with CSG-PL-syst.[131] fractal image An improved GA of solving IFS code of
•
[116] fractals Evolving •
[70] frequency Automated parameter opt. for douple •
modulation synthesis using the gen. annealing alg.[69] – Automatic parameter opt. for double • modulation
synthesis using the gen. annealing alg.[78] functional Using physically-based models and GAs for
• composition of sound signals, synchronized to animated mo-tion
[41] funny Disney meets Darwin – the evol. of • animatedfigures
[105] fused Evaluation of GA-generated multivariate color ta-bles for the visualization of multimodal medical • data sets
[30] Future Geneesys – katsaus kolmiulotteiseen kei-noelamaan nyt ja hahmotelma tulevaisuudesta [A Review of 3D AL and Outline of its •
[122] fuzzy A • cntr. rendering syst. for virtual reality syst.optimised by GAs
[87] GA-based An automatic • construction of neural net-works for motion cntr. of virtual life
[146] – Interactive • design support syst. for lighting design in3-D computer graphics
[125] – Interactive • design support syst. for lighting design incomputer graphics
[65] • noisy speech recognition using two-dimensional cep-strum
[58] garden In the infinity of computer space there is a • of unearthly delights
[1] GArphics - Applying GAs for generating graphics[30] Geneesys – katsaus kolmiulotteiseen keinoelamaan nyt
ja hahmotelma tulevaisuudesta [A Review of 3D AL and Out-line of its Future]
[130] generated Using GAs to improve the visual quality of fractal plants • with CSG-PL-syst.
[1] generating GArphics - Applying GAs for • graphics[167] – GenJam: A GA for • jazz solos[184] – Method and device for • musical sound waveform[166] • rhytms with GAs[182] generation A GA for the • of jazz melodies[173] – A grammar based gen. prog. technique appl. to music
•
[168] – GAs in musical style oriented •
[123] – Gen. prog. for easy 3D texture •
[25] – Roof shape • method for buildings using KANSEI eval-uation rules
[26] Generic building product model incorporating buildingtype info
[167] GenJam A GA for generating jazz solos[177] GenJam An interactive GA jazz improviser[174] GeNotator An environment for investigating the appl.
of GAs in computer assisted composition[44] geometric primitives FormSynth: The rule-based
evol. of complex forms from •
[60] getting Alkavatko taideteoksetkin elaa? [Is art • life?][136] global A neuro-evol. unbiased • illumination alg.[77] • Opt. for Articulated Figures: Molecular Structure
Prediction and Motion Synthesis for Animation[115] – Simulation of • Illumination: An Evol. Appr.[85] Gnats Keinoelamaa virtuaalitodellisuudessa – hyttysia
ja muita otokoita [Artificial life in virtual reality – • and otherlittle creatures]
[173] grammar A • based gen. prog. technique appl. tomusic generation
[158, 160] grammatical Automatic composition of music bymeans of • evol.
[99] • evol. to design fractal curves with a given dimension[121] graph Automatic • drawing by gen. search[114] – Evol. learning of • layout constraints from examples[143] – Gen. • drawing[94] Graphic object layout with interactive GAs[49] graphics Artificial evol. for computer •[1] – GArphics - Applying GAs for generating •
[125] – Interactive GA-based design support syst. for lightingdesign in computer •
[17] graphs A GA for drawing undirected •
[35] gray-scale FPGA realisation of the GA for the designof • soft morphological filters
[34] grayscale GA opt. of multidimensional • soft morpho-logical filters with appl. in film archive restoration
[120] ground Appl. of GA to design of artificial •[71] Group synthesis with GAs[145] growth Lifelike artificial trees based on • iterated func-
tion syst.[30] hahmotelma Geneesys – katsaus kolmiulotteiseen kei-
noelamaan nyt ja • tulevaisuudesta [A Review of 3D AL andOutline of its Future]
[59] hajoavat Kaarmemaiset sykkyrat pyorivat, • ja kulke-vat itsensa lapi, Tietokonetaiteilija William Latham luoolioitaan evoluution saantojen avulla [Refers to works of com-puter artist William Latham]
[170] Harmonisation of musical progression with GAs
[169] harmony Bach in a box: The evol. of four part baroque• using the GA
[67] Hearing Aid Fitting with GAs[21] House design syst. using GA[18, 19 ] House design syst. using GA[67] HRTFs Efficient model fitting using a GA: pole-zero ap-
proximations of •[54] human Mutator, a subjective • interface for evol. of
computer sculptures[79] humanoid Motion design of a 3D-CG avatar that uses
• animation[180] hybrid A • neuro-gen. pattern evol. syst. appl. to
musical composition[74] • sampling-wavetable synthesis with GAs
8/10/2019 Bibliography of Genetic Algorithms in Arts and Music
http://slidepdf.com/reader/full/bibliography-of-genetic-algorithms-in-arts-and-music 27/49
Permuted title index 23
[85] hyttysia Keinoelamaa virtuaalitodellisuudessa – • jamuita otokoita [Artificial life in virtual reality – Gnats andother little creatures]
[15] ideal Evolving attractive faces using morphing technol-ogy and a GA: A new appr. to determining • facial aesthetics
[86] identification Evol. • of cloth animation models[131] IFS code An improved GA of solving • of fractal image[175] IGA Neural network fitness functions for a musical •[136] illumination A neuro-evol. unbiased global • alg.[115] – Simulation of Global • An Evol. Appr.[96] Illustrating Evol. Computation with Mathematica[61] ilmioiden Tietokonetaide on monien • leikkauspiste
[Computer art][36] image Automatic design support and • evaluation of
two-coloured products using colour association and colour har-mony scales and GA
[84] – Building new tools for synthetic • animation by usingevol. techniques
[140] – Recovery of superquadric primitives from a range • us-ing GA
[32] images Evol. of digital •[53] – Evolving •
[24] – Lighting quality research using rendered • of offices[128] – Shape modeling of multiple objects from shading • us-
ing GAs[133] – Superquadrics modeling of multiple objects from shad-
ing • using GAs[39] – Towards automated artificial evol. for computer-
generated •
[177] improviser GenJam: An interactive GA jazz •
[58] infinity In the • of computer space there is a garden of unearthly delights
[26] information Generic building product model incorpo-rating building type •
[63] instrument Methods for multiple wavetable synthesisof musical • tones
[76] – Nested modulator and feedback FM matching of •tones
[68] instruments Wavetable matching synthesis of dynamic• with GAs
[118] integration An evol. model for • problems[161] intelligent Evolving • musical materials[138] interactive 3-D CG lighting with an • GA[31] – Appl. of • GA to fashion design[177] – GenJam: An • GA jazz improviser[94] – Graphic object layout with • GAs[50] • evol. of dynamical syst.[51, 52] • evol. of equations for procedural models[119] • evol. alg. in design[146] • GA-based design support syst. for lighting design in
3-D computer graphics[125] • GA-based design support syst. for lighting design in
computer graphics[33] interactive)art AI in contemporary •
[54] interface Mutator, a subjective human • for evol. of computer sculptures
[73] interpolation Computation and memory tradeoffs withmultiple wavetable •
[174] investigating GeNotator: An environment for • theappl. of GAs in computer assisted composition
[145] iterated function system Lifelike artificial treesbased on growth •
[59] itsensa Kaarmemaiset sykkyrat pyorivat, hajoavat jakulkevat • lapi, Tietokonetaiteilija William Latham luoolioitaan evoluution saantojen avulla [Refers to works of com-puter artist William Latham]
[182] jazz A GA for the generation of • melodies[167] – GenJam: A GA for generating • solos[177] – GenJam: An interactive GA • improviser[25] KANSEI Roof shape generation method for buildings
using • evaluation rules[30] katsaus Geneesys – • kolmiulotteiseen keinoelamaan
nyt ja hahmotelma tulevaisuudesta [A Review of 3D AL andOutline of its Future]
[85] Keinoelamaa virtuaalitodellisuudessa – hyttysia jamuita otokoita [Artificial life in virtual reality – Gnats andother little creatures]
[30] keinoelamaan Geneesys – katsaus kolmiulotteiseen •
nyt ja hahmotelma tulevaisuudesta [A Review of 3D AL andOutline of its Future]
[20] kitchen Data structure for syst. • editing and designing
[30] kolmiulotteiseen Geneesys – katsaus • keinoelamaannyt ja hahmotelma tulevaisuudesta [A Review of 3D AL andOutline of its Future]
[59] kulkevat Kaarmemaiset sykkyrat pyorivat, hajoavat ja • itsensa lapi, Tietokonetaiteilija William Latham luoolioitaan evoluution saantojen avulla [Refers to works of com-puter artist William Latham]
[141] labels Placing text • on maps and diagrams using GAswith masking
[106] landscapes Modelling video games’ • by means of gen.terrain prog. - A new appr. for improving users’ experience
[59] Latham Kaarmemaiset sykkyrat pyorivat, hajoavat jakulkevat itsensa lapi, Tietokonetaiteilija William Latham luoolioitaan evoluution saantojen avulla [Refers to works of com-puter artist William •
[59] – Kaarmemaiset sykkyrat pyorivat, hajoavat ja kulke-vat itsensa lapi, Tietokonetaiteilija William • luo olioitaanevoluution saantojen avulla [Refers to works of computer artistWilliam Latham]
[108] layout Automating the • of network diagrams with spec-ified visual organization
[114] – Evol. learning of graph • constraints from examples[94] – Graphic object • with interactive GAs[142] – Opt. of flowsheet drawing • using a GA[114] learning Evol. • of graph layout constraints from ex-
amples[28] • emergent style using an evol. appr.[61] leikkauspiste Tietokonetaide on monien ilmioiden •
[Computer art][27] levels Artificial intelligence appr. to the prediction of
nat. lighting •
[60] life Alkavatko taideteoksetkin elaa? [Is art getting •
[87] – An automatic GA-based construction of neural net-works for motion cntr. of virtual •
[30] – Geneesys – katsaus kolmiulotteiseen keinoelamaan nyt ja hahmotelma tulevaisuudesta [A Review of 3D Artificial •
and Outline of its Future][145] Lifelike artificial trees based on growth iterated func-
tion syst.[138] lighting 3-D CG • with an interactive GA[27] – Artificial intelligence appr. to the prediction of nat. •
levels[146] – Interactive GA-based design support syst. for • design
in 3-D computer graphics[125] – Interactive GA-based design support syst. for • design
in computer graphics[24] • quality research using rendered images of offices[23] – Opt. design of outdoor • syst. by GAs[144] Lindenmayer On GAs and • syst.[38] line drawings Evolving •
[124] L-system Gen. • prog. : breeding and evolving artifi-cial flowers with Mathematica
[107] – Gen. • prog.[149] L-system Morphogenesis of path plan sequences
through gen. synthesis of • productions[126] L-systems Evolving evol. prog. : Gen. prog. and •
[93] – Patterns of cluster formation and evol. activity inevolving •
[22] luminances Preferred surface • in offices, by evol. : apilot study
[59] luo Kaarmemaiset sykkyrat pyorivat, hajoavat ja kulke-vat itsensa lapi, Tietokonetaiteilija William Latham •
olioitaan evoluution saantojen avulla [Refers to works of com-puter artist William Latham]
[178] machina Musica ex • composing 16th-century counter-point with gen. prog. and symbiosis
[156] Machine tongues XVI. GAs and their appl. to FM
matching synthesis[141] maps Placing text labels on • and diagrams using GAs
with masking[141] masking Placing text labels on maps and diagrams us-
ing GAs with •
[156] matching Machine tongues XVI. GAs and their appl.to FM • synthesis
[76] – Nested modulator and feedback FM • of instrumenttones
[68] – Wavetable • synthesis of dynamic instruments withGAs
[161] materials Evolving intelligent musical •[135] Mathematica Principia Evolvica, Simulierte Evol.
mit •
8/10/2019 Bibliography of Genetic Algorithms in Arts and Music
http://slidepdf.com/reader/full/bibliography-of-genetic-algorithms-in-arts-and-music 28/49
24 Genetic algorithms in arts and music
[124] Mathematica Gen. L-syst. prog. : breeding andevolving artificial flowers with •
[96] Mathematica Illustrating Evol. Computation with •
[117] MathEvolvica – Simulierte Evol. von Entwick-lungsprog. men der Natur
[105] medical Evaluation of GA-generated multivariate colortables for the visualization of multimodal • fused data sets
[41] meets Disney • Darwin – the evol. of funny animatedfigures
[182] melodies A GA for the generation of jazz •
[73] memory Computation and • tradeoffs with multiplewavetable interpolation
[137] model A two-stage evol. • for the computer-aided de-sign of colour combinations
[118] – An evol. • for integration problems[67] – Efficient • fitting using a GA: pole-zero approximations
of HRTFs[80] modeling Evol. alg. in • and animation[171] – Sel. of attributed for • Bach chorales by a GA[128] – Shape • of multiple objects from shading images using
GAs[133] – Superquadrics • of multiple objects from shading im-
ages using GAs[106] Modelling video games’ landscapes by means of gen.
terrain prog. - A new appr. for improving users’ experience[88] – Three-dimensional colour image and animation • for
CAL[101] models Building photorealistic • using data fusion[102] – Creating photorealistic • by data fusion with GAs[86] – Evol. ident. of cloth animation •
[51, 52] – Interactive evol. of equations for procedural •[104] – Photorealistic 3D • of Real-World Objects[78] – Using physically-based • and GAs for functional com-
position of sound signals, synchronized to animated motion[70] modulation Automated parameter opt. for douple fre-
quency • synthesis using the gen. annealing alg.[69] – Automatic parameter opt. for double frequency • syn-
thesis using the gen. annealing alg.[76] modulator Nested • and feedback FM matching of in-
strument tones[77] Molecular Global Opt. for Articulated Figures: •
Structure Prediction and Motion Synthesis for Animation[61] monien Tietokonetaide on • ilmioiden leikkauspiste
[Computer art][15] morphing Evolving attractive faces using • technology
and a GA: A new appr. to determining ideal facial aesthetics[149] Morphogenesis of path plan sequences through gen.
synthesis of L-syst. productions[35] morphological FPGA realisation of the GA for the de-
sign of gray-scale soft • filters[34] – GA opt. of multidimensional grayscale soft • filters
with appl. in film archive restoration[87] motion An automatic GA-based construction of neural
networks for • cntr. of virtual life[82] – Gen. prog. for articulated figure •
[77] – Global Opt. for Articulated Figures: Molecular Struc-ture Prediction and • Synthesis for Animation
[92] • blending using a CS[79] • design of a 3D-CG avatar that uses humanoid anima-
tion[78] – Using physically-based models and GAs for functional
composition of sound signals, synchronized to animated •
[132] movies Evolving fractal •[85] muita Keinoelamaa virtuaalitodellisuudessa – hyttysia
ja • otokoita [Artificial life in virtual reality – Gnats and otherlittle creatures]
[34] multidimensional GA opt. of • grayscale soft mor-phological filters with appl. in film archive restoration
[105] multimodal Evaluation of GA-generated multivariatecolor tables for the visualization of • medical fused data sets
[105] multivariate Evaluation of GA-generated • color tablesfor the visualization of multimodal medical fused data sets
[173] music A grammar based gen. prog. technique appl. to• generation
[163] – A novel appr. to automatic • transcription using elec-tronic synthesis and GAs
[158, 160] – Automatic composition of • by means of grammat-ical evol.
[164] – Electronic synthesis using GAs for automatic • tran-scription
[165] – Evol. alg. and automatic transcription of •
[155] – GA composes •
[152, 153, 154] – GAs and computer-assisted • composition[185] – Gen. • compositor[43] – Indexed Bibliography of GAs in Art and •
[159] • recognition syst. using ART-1 and GA[157] – Neurogen, • composition using GAs and cooperating
neural networks[178] Musica ex machina: composing 16th-century counter-
point with gen. prog. and symbiosis[180] musical A hybrid neuro-gen. pattern evol. syst. appl.
to • composition[181] – Evol. methods for • composition[161] – Evolving intelligent • materials[179] – GA utilising neural network fitness evaluation for •
composition[170] – Harmonisation of • progression with GAs[184] – Method and device for generating • sound waveform[63] – Methods for multiple wavetable synthesis of • instru-
ment tones[175] – Neural network fitness functions for a • IGA[72] musical instrument Discrete summation synthesis of
• tones using GAs[168] musical style GAs in • oriented generation[54] Mutator a subjective human interface for evol. of com-
puter sculptures[117] Natur MathEvolvica – Simulierte Evol. von Entwick-
lungsprog. men der •
[27] natural Artificial intelligence appr. to the prediction of • lighting levels
[76] Nested modulator and feedback FM matching of instru-ment tones
[108] network Automating the layout of • diagrams withspecified visual organization
[139] neural nets Where to fish for •
[179] neural network GA utilising • fitness evaluation formusical composition
[175] • fitness functions for a musical IGA[87] neural networks An automatic GA-based construc-
tion of • for motion cntr. of virtual life[157] – Neurogen, music composition using GAs and cooper-
ating •
[75] – Training partially recurrent • using evol. strategies[136] neuro-evolutionary A • unbiased global illumination
alg.[157] Neurogen music composition using GAs and cooperat-
ing neural networks[180] neuro-genetic A hybrid • pattern evol. syst. appl. to
musical composition[65] noisy GA-based • speech recognition using two-
dimensional cepstrum[30] nyt Geneesys – katsaus kolmiulotteiseen keinoelamaan •
ja hahmotelma tulevaisuudesta [A Review of 3D AL and Out-line of its Future]
[94] object Graphic • layout with interactive GAs[100] objects Evol. design of • using scene graphs[104] – Photorealistic 3D Models of Real-World •
[128] – Shape modeling of multiple • from shading images us-ing GAs
[133] – Superquadrics modeling of multiple • from shading im-ages using GAs
[24] offices Lighting quality research using rendered imagesof •
[22] – Preferred surface luminances in • by evol. : a pilotstudy
[59] olioitaan Kaarmemaiset sykkyrat pyorivat, hajoavat jakulkevat itsensa lapi, Tietokonetaiteilija William Latham luo• evoluution saantojen avulla [Refers to works of computer
artist William Latham][23] Optimal design of outdoor lighting syst. by GAs[122] optimised A fuzzy cntr. rendering syst. for virtual re-
ality syst. • by GAs[151] Optimized connection of rational surface-based on GAs[183] Optimizing additive synthesis parameters with GAs
and self-organizing maps[129] Organic art[62] Organisms Self-evolving arts— • versus fetishes[108] organization Automating the layout of network dia-
grams with specified visual •
[168] oriented GAs in musical style • generation[66] otoneurological Experiances of • expert syst. for ver-
tigo
8/10/2019 Bibliography of Genetic Algorithms in Arts and Music
http://slidepdf.com/reader/full/bibliography-of-genetic-algorithms-in-arts-and-music 29/49
Permuted title index 25
[23] outdoor Opt. design of • lighting syst. by GAs[48] Panspermia[70] parameter Automated • opt. for douple frequency
modulation synthesis using the gen. annealing alg.[69] – Automatic • opt. for double frequency modulation syn-
thesis using the gen. annealing alg.[98] parameters Estimating • for procedural texturing by
GAs[183] – Opt. additive synthesis • with GAs and self-organizing
maps[75] partially Training • recurrent neural networks using
evol. strategies[149] path Morphogenesis of • plan sequences through gen.
synthesis of L-syst. productions[180] pattern A hybrid neuro-gen. • evol. syst. appl. to
musical composition[93] Patterns of cluster formation and evol. activity in
evolving L-syst.[64] peak Low • amplitudes for wavetable synthesis[103] perceptual An appr. to the • opt. of complex visual-
izations[101] photorealistic Building • models using data fusion[102] – Creating • models by data fusion with GAs[104] • 3D Models of Real-World Objects[81] physically Automatic cntr. of • realistic animated fig-
ures using EP[78] physically-based Using • models and GAs for func-
tional composition of sound signals, synchronized to animatedmotion
[22] pilot Preferred surface luminances in offices, by evol. :a • study
[141] Placing text labels on maps and diagrams using GAswith masking
[149] plan Morphogenesis of path • sequences through gen.synthesis of L-syst. productions
[130] plants Using GAs to improve the visual quality of frac-tal • generated with CSG-PL-syst.
[67] pole-zero Efficient model fitting using a GA: • approx-imations of HRTFs
[97] polygonal approximation An efficient evol. alg. foraccurate •
[27] prediction Artificial intelligence appr. to the • of nat.lighting levels
[77] – Global Opt. for Articulated Figures: Molecular Struc-ture • and Motion Synthesis for Animation
[22] Preferred surface luminances in offices, by evol. : apilot study
[140] primitives Recovery of superquadric • from a range im-age using GA
[135] Principia Evolvica, Simulierte Evol. mit Mathematica
[98] procedural Estimating parameters for • texturing byGAs
[51, 52] – Interactive evol. of equations for • models[91] process Animating the evol. • of GAs[47] processes The appl. of evol. and biological • to com-
puter art and animation[37] product Automatic • color design using gen. searching[29] – GA search efficacy in aesthetic • spaces[26] product model Generic building • incorporating
building type info[149] productions Morphogenesis of path plan sequences
through gen. synthesis of L-syst. •[36] products Automatic design support and image evalu-
ation of two-coloured • using colour association and colourharmony scales and GA
[81] programming Automatic cntr. of physically realisticanimated figures using evol. •
[124] – Gen. L-syst. • breeding and evolving artificial flowerswith Mathematica
[107] – Gen. L-syst. •
[106] – Modelling video games’ landscapes by means of gen.terrain • - A new appr. for improving users’ experience
[170] progression Harmonisation of musical • with GAs[59] pyorivat Kaarmemaiset sykkyrat • hajoavat ja kulkevat
itsensa lapi, Tietokonetaiteilija William Latham luo olioitaanevoluution saantojen avulla [Refers to works of computer artistWilliam Latham]
[24] quality Lighting • research using rendered images of of-fices
[130] – Using GAs to improve the visual • of fractal plantsgenerated with CSG-PL-syst.
[140] range Recovery of superquadric primitives from a • im-age using GA
[151] rational Optimized connection of • surface-based onGAs
[109] ray tracing Rayvolution: an evol. • alg.[112] – Rayvolution: An evol. • alg.[109] Rayvolution an evol. ray tracing alg.[112] • An evol. ray tracing alg.[35] realisation FPGA • of the GA for the design of gray-
scale soft morphological filters[81] realistic Automatic cntr. of physically • animated fig-
ures using EP[85] reality Keinoelamaa virtuaalitodellisuudessa – hyttysia
ja muita otokoita [Artificial life in virtual • – Gnats and otherlittle creatures]
[104] Real-World Photorealistic 3D Models of • Objects[65] recognition GA-based noisy speech • using two-
dimensional cepstrum[159] – Music • syst. using ART-1 and GA[140] Recovery of superquadric primitives from a range im-
age using GA[75] recurrent Training partially • neural networks using
evol. strategies[59] Refers Kaarmemaiset sykkyrat pyorivat, hajoavat ja
kulkevat itsensa lapi, Tietokonetaiteilija William Latham luoolioitaan evoluution saantojen avulla • to works of computerartist William Latham]
[24] rendered Lighting quality research using • images of offices
[122] rendering A fuzzy cntr. • syst. for virtual reality syst.optimised by GAs
[110] – Approximation der • Equation durch Evol. are Alg. en[24] research Lighting quality • using rendered images of of-
fices[34] restoration GA opt. of multidimensional grayscale soft
morphological filters with appl. in film archive •
[30] Review Geneesys – katsaus kolmiulotteiseen kei-noelamaan nyt ja hahmotelma tulevaisuudesta [A • of 3D ALand Outline of its Future]
[166] rhytms Generating • with GAs[25] Roof shape generation method for buildings using KAN-
SEI evaluation rules[44] rule-based FormSynth: The • evol. of complex forms
from geometric primitives[25] rules Roof shape generation method for buildings using
KANSEI evaluation •
[74] sampling-wavetable Hybrid • synthesis with GAs[36] scales Automatic design support and image evaluation
of two-coloured products using colour association and colourharmony • and GA
[100] scene graphs Evol. design of objects using •
[56] sculpture Computer • design and animation[54] sculptures Mutator, a subjective human interface for
evol. of computer •
[45, 46] • in the void[121] search Automatic graph drawing by gen. •[29] – GA • efficacy in aesthetic product spaces[37] searching Automatic product color design using gen. •[83] • for facial expression by GA[42] selection Evol. strategies with subjective •
[171] • of attributed for modeling Bach chorales by a GA[62] Self-evolving arts— Organisms versus fetishes[183] self-organizing maps Opt. additive synthesis param-
eters with GAs and •
[149] sequences Morphogenesis of path plan • through gen.synthesis of L-syst. productions
[105] sets Evaluation of GA-generated multivariate color ta-bles for the visualization of multimodal medical fused data •
[128] shading Shape modeling of multiple objects from • im-ages using GAs
[133] – Superquadrics modeling of multiple objects from • im-ages using GAs
[172] shape A drum • opt. by GAs[128] • modeling of multiple objects from shading images us-
ing GAs[127] • transformation in space-time[25] – Roof • generation method for buildings using KANSEI
evaluation rules[78] signals Using physically-based models and GAs for
functional composition of sound • synchronized to animatedmotion
8/10/2019 Bibliography of Genetic Algorithms in Arts and Music
http://slidepdf.com/reader/full/bibliography-of-genetic-algorithms-in-arts-and-music 30/49
26 Genetic algorithms in arts and music
[115] Simulation of Global Illumination: An Evol. Appr.[117] Simulierte MathEvolvica – • Evol. von Entwick-
lungsprog. men der Natur[135] – Principia Evolvica, • Evol. mit Mathematica[35] soft FPGA realisation of the GA for the design of gray-
scale • morphological filters[34] – GA opt. of multidimensional grayscale • morphological
filters with appl. in film archive restoration[167] solos GenJam: A GA for generating jazz •
[162] sonic Evolving • ecosyst.[184] sound Method and device for generating musical • wave-
form[78] – Using physically-based models and GAs for functional
composition of • signals, synchronized to animated motion[58] space In the infinity of computer • there is a garden of
unearthly delights[29] spaces GA search efficacy in aesthetic product •
[127] space-time Shape transformation in •
[108] specified Automating the layout of network diagramswith • visual organization
[65] speech GA-based noisy • recognition using two-dimensional cepstrum
[111] string A cat’s cradle • diagram display method basedon a GA
[20] structure Data • for syst. kitchen editing and design-ing
[77] – Global Opt. for Articulated Figures: Molecular • Pre-diction and Motion Synthesis for Animation
[16] structures Appl. of GA to aesthetic design of bridge •
[28] style Learning emergent • using an evol. appr.[42] subjective Evol. strategies with • sel.[54] – Mutator, a • human interface for evol. of computer
sculptures[72] summation Discrete • synthesis of musical instrument
tones using GAs[140] superquadric Recovery of • primitives from a range
image using GA[133] Superquadrics modeling of multiple objects from
shading images using GAs[36] support Automatic design • and image evaluation of
two-coloured products using colour association and colour har-mony scales and GA
[146] – Interactive GA-based design • syst. for lighting designin 3-D computer graphics
[125] – Interactive GA-based design • syst. for lighting designin computer graphics
[22] surface Preferred • luminances in offices, by evol. : apilot study
[151] surface-based Optimized connection of rational • onGAs
[55] surreal Artificial life or • art?[59] sykkyrat Kaarmemaiset • pyorivat, hajoavat ja kulke-
vat itsensa lapi, Tietokonetaiteilija William Latham luoolioitaan evoluution saantojen avulla [Refers to works of com-puter artist William Latham]
[178] symbiosis Musica ex machina: composing 16th-centurycounterpoint with gen. prog. and •
[78] synchronized Using physically-based models and GAsfor functional composition of sound signals, • to animated mo-tion
[163] synthesis A novel appr. to automatic music transcrip-tion using electronic • and GAs
[70] – Automated parameter opt. for douple frequency mod-ulation • using the gen. annealing alg.
[69] – Automatic parameter opt. for double frequency mod-ulation • using the gen. annealing alg.
[72] – Discrete summation • of musical instrument tones us-
ing GAs[164] – Electronic • using GAs for automatic music transcrip-
tion[77] – Global Opt. for Articulated Figures: Molecular Struc-
ture Prediction and Motion • for Animation[71] – Group • with GAs[74] – Hybrid sampling-wavetable • with GAs[64] – Low peak amplitudes for wavetable •
[156] – Machine tongues XVI. GAs and their appl. to FMmatching •
[63] – Methods for multiple wavetable • of musical instrumenttones
[149] – Morphogenesis of path plan sequences through gen. •
of L-syst. productions
[183] – Opt. additive • parameters with GAs and self-organizing maps
[68] – Wavetable matching • of dynamic instruments withGAs
[84] synthetic Building new tools for • image animation byusing evol. techniques
[60] taideteoksetkin Alkavatko • elaa? [Is art getting life?][84] techniques Building new tools for synthetic image an-
imation by using evol. •
[15] technology Evolving attractive faces using morphing •
and a GA: A new appr. to determining ideal facial aesthetics[106] terrain Modelling video games’ landscapes by means of
gen. • prog. - A new appr. for improving users’ experience[141] text Placing • labels on maps and diagrams using GAs
with masking[123] texture Gen. prog. for easy 3D • generation[98] texturing Estimating parameters for procedural • by
GAs[88] Three-dimensional colour image and animation mod-
elling for CAL[61] Tietokonetaide on monien ilmioiden leikkauspiste
[Computer art][59] Tietokonetaiteilija Kaarmemaiset sykkyrat pyorivat,
hajoavat ja kulkevat itsensa lapi, • William Latham luoolioitaan evoluution saantojen avulla [Refers to works of com-puter artist William Latham]
[176] tone Common • adaptive tuning using GAs[72] tones Discrete summation synthesis of musical instru-
ment • using GAs[63] – Methods for multiple wavetable synthesis of musical in-
strument •
[76] – Nested modulator and feedback FM matching of instru-ment •
[156] tongues Machine • XVI. GAs and their appl. to FMmatching synthesis
[84] tools Building new • for synthetic image animation byusing evol. techniques
[73] tradeoffs Computation and memory • with multiplewavetable interpolation
[75] Training partially recurrent neural networks using evol.strategies
[163] transcription A novel appr. to automatic music • us-ing electronic synthesis and GAs
[164] – Electronic synthesis using GAs for automatic music •
[165] – Evol. alg. and automatic • of music[127] transformation Shape • in space-time
[145] trees Lifelike artificial •
based on growth iterated func-tion syst.[30] tulevaisuudesta Geneesys – katsaus kolmiulotteiseen
keinoelamaan nyt ja hahmotelma • [A Review of 3D AL andOutline of its Future]
[176] tuning Common tone adaptive • using GAs[36] two-coloured Automatic design support and image
evaluation of • products using colour association and colourharmony scales and GA
[65] two-dimensional GA-based noisy speech recognitionusing • cepstrum
[137] two-stage A • evol. model for the computer-aided de-sign of colour combinations
[26] type Generic building product model incorporatingbuilding • info
[136] unbiased A neuro-evol. • global illumination alg.[17] undirected A GA for drawing • graphs[58] unearthly In the infinity of computer space there is a
garden of • delights
[106] users Modelling video games’ landscapes by means of gen. terrain prog. - A new appr. for improving • experience
[179] utilising GA • neural network fitness evaluation for mu-sical composition
[66] vertigo Experiances of otoneurological expert syst. for•
[106] video games Modelling • landscapes by means of gen.terrain prog. - A new appr. for improving users’ experience
[85] virtuaalitodellisuudessa Keinoelamaa • – hyttysia ja muita otokoita [Artificial life in virtual reality – Gnats andother little creatures]
[87] virtual An automatic GA-based construction of neuralnetworks for motion cntr. of • life
[113] – Evolving • creatures
8/10/2019 Bibliography of Genetic Algorithms in Arts and Music
http://slidepdf.com/reader/full/bibliography-of-genetic-algorithms-in-arts-and-music 31/49
8/10/2019 Bibliography of Genetic Algorithms in Arts and Music
http://slidepdf.com/reader/full/bibliography-of-genetic-algorithms-in-arts-and-music 32/49
8/10/2019 Bibliography of Genetic Algorithms in Arts and Music
http://slidepdf.com/reader/full/bibliography-of-genetic-algorithms-in-arts-and-music 33/49
Bibliography
[1] Anssi Jantti and Jarmo T. Alander. GArphics - applying genetic algorithms for generating graphics. InTapio Pahikkala, Jaakko Vayrynen, Jukka Kortela, and Antti Airola, editors, Proceedings of the 14th Finnish Artificial Intelligence Conference STeP 2010 , pages 39–45, Espoo (Finland), 17.-18. August 2010. FinnishArtificial Intelligence Society. ga10aAnssiJantti ⇒ http://www.stes.fi/step2010/program.html.
[2] John H. Holland. Genetic algorithms. Scientific American , 267(1):44–50, 1992. ga:Holland92a.
[3] Jarmo T. Alander. An indexed bibliography of genetic algorithms: Years 1957-1993 . Art of CAD Ltd., Vaasa(Finland), 1994. (over 3000 GA references).
[4] David E. Goldberg, Kelsey Milman, and Christina Tidd. Genetic algorithms: A bibliography. IlliGAL
Report 92008, University of Illinois at Urbana-Champaign, 1992. ga:Goldberg92f.
[5] N. Saravanan and David B. Fogel. A bibliography of evolutionary computation & applications. TechnicalReport FAU-ME-93-100, Florida Atlantic University, Department of Mechanical Engineering, 1993. (ftp:
//magenta.me.fau.edu/pub/ep-list/bib/EC-ref.ps.Z) ga:Fogel93c.
[6] Thomas Back. Genetic algorithms, evolutionary programming, and evolutionary strategies bibliographicdatabase entries. (personal communication) ga:Back93bib, 1993.
[7] Thomas Back, Frank Hoffmeister, and Hans-Paul Schwefel. Applications of evolutionary algorithms. Tech-nical Report SYS-2/92, University of Dortmund, Department of Computer Science, 1992. ga:Schwefel92d.
[8] David L. Hull. Uncle Sam wants you. Science , 284(5417):1131–1133, 14. May 1999.
[9] Leslie Lamport. LAT E X: A Document Preparation System. User’s Guide and Reference manual . Addison-Wesley Publishing Company, Reading, MA, 2 edition, 1994.
[10] Alfred V. Aho, Brian W. Kernighan, and Peter J. Weinberger. The AWK Programming Language . Addison-Wesley Publishing Company, Reading, MA, 1988.
[11] Diane Barlow Close, Arnold D. Robbins, Paul H. Rubin, and Richard Stallman. The GAWK Manual .Cambridge, MA, 0.15 edition, April 1993.
[12] Jarmo T. Alander. Indexed bibliography of genetic algorithms in optics and image processing. Report 94-1-OPTICS, University of Vaasa, Department of Information Technology and Production Economics, 1995.http://lipas.uwasa.fi/∼TAU/reports/report94-1/gaOPTICSbib.pdf gaOPTICSbib.
[13] Jarmo T. Alander. Indexed bibliography of genetic algorithms in signal and image processing. Report 94-1-SIGNAL, University of Vaasa, Department of Information Technology and Production Economics, 1995.http://lipas.uwasa.fi/∼TAU/reports/report94-1/gaSIGNALbib.pdf gaSIGNALbib.
[14] Jarmo T. Alander. Indexed bibliography of genetic algorithms in computer aided design. Report 94-1-CAD, University of Vaasa, Department of Information Technology and Production Economics, 1995.http://lipas.uwasa.fi/∼TAU/reports/report94-1/gaCADbib.pdf gaCADbib.
[15] Brian J. F. Wong, Koohyar Karimi, Zlatko Devcic, CVhristine E. McLaren, and Wen-Pin Chen. Evolvingattractive faces using morphing technology and a genetic algorithm: A new approach to determining idealfacial aesthetics. Laryngoscope , 118(6):962–974, June 2008. * ga08aBrianJFWong.
[16] H. Furuta, K. Maeda, and E. Watanabe. Application of genetic algorithm to aesthetic design of bridgestructures. Microcomputers in Civil Engineering , 10(6):415–421, 1995. †CCA96766/95 ga95bFuruta.
[17] Jurgen Branke, Frank Bucher, and Hartmut Schmeck. A genetic algorithm for drawing undirected graphs.In Alander [195], pages 193–206. (ftp://ftp.uwasa.fics/3NWGA/Branke.ps.Z) ga97aBranke.
[18] Masashi Kuriwaki and Tokifumi Kubai. House design system using genetic algorithm, 2000. (JP patent no.2000285149. Issued October 13 2000) * fi.espacenet.com ga00aMKuriwaki.
29
8/10/2019 Bibliography of Genetic Algorithms in Arts and Music
http://slidepdf.com/reader/full/bibliography-of-genetic-algorithms-in-arts-and-music 34/49
30 Genetic algorithms in arts and music
[19] Masashi Kuriwaki and Tokifumi Kubai. House design system using genetic algorithm, 2000. (JP patent no.2000285149. Issued October 13 2000) * fi.espacenet.com ga00bMKuriwaki.
[20] Masanori Kashiwagi. Data structure for system kitchen editing and designing, 2001. (JP patent no.2001175638. Issued June 29 2001) * fi.espacenet.com ga01aMKashiwagi.
[21] Masashi Kuriwaki and Tokifumi Kubai. House design system using genetic algorithm, 2001. (JP patent no.2001084285. Issued March 30 2001) * fi.espacenet.com ga01bMKuriwaki.
[22] G. R. Newsham, R. G. Marchand, and J. A. Veitch. Preferred surface luminances in offices, by evolution:a pilot study. In ?, editor, Proceedings of the IESNA Annual Conference , pages 375–398, Salt Lake City,5.-7. August 2002. ? ga02aGRNewsham.
[23] M. Corcione and L. Fontana. Optimal design of outdoor lighting systems by genetic algorithms. Lighting Research and Technology , 35(3):261–280, ? 2003. ga03aMCorcione.
[24] G. R. Newsham, C. Richardson, C. Blanchet, and J. A. Veitch. Lighting quality research using renderedimages of offices. Lighting Research and Technology , 37(2):93–115, ? 2005. ga05aGRNewsham.
[25] Kazutoshi Tsutsumi, Yasuharu Omori, and Keisuke Sasaki. Roof shape generation method for buildingsusing KANSEI evaluation rules. In 2006 International Symposium on Evolving Fuzzy Systems , pages 306–311, ?, September 2006. IEEE, Piscataway, NJ. ga06aKTsutsumi.
[26] Charles M. Eastman and Anastassios Siabiris. Generic building product model incorporating building typeinformation. Autom. Constr., 3(4):238–304, January 1995. * EI M092184/95 ga95aEastman.
[27] D. A. Coley and J. A. Crabb. Artificial intelligence approach to the prediction of natural lighting levels.Build Environment , 32(2):81–85, 1997. †EI M157095/97 ga97aDAColey.
[28] John S. Gero and Lan Ding. Learning emergent style using an evolutionary approach. In Michael Blumen-stein, editor, Proceedings of the International Conference on Computational Intelligence and Multimedia Applications , pages 171–175, Gold Coast, QUE, Australia, February 1997. Watson Ferguson & Company(Griffith University). ga97aJSGero.
[29] D. A. Coley and D. Winters. Genetic algorithm search efficacy in aesthetic product spaces. Complexity (USA), 3(2):23–27, 1997. †CCA25193/98 ga97bDAColey.
[30] Tomi Salminen. Geneesys – katsaus kolmiulotteiseen keinoelamaan nyt ja hahmotelma tulevaisuudesta [Areview of 3D artificial life and outline of its future]. Master’s thesis, University of Industrial Arts Helsinki,Media Laboratory; Taideteollinen korkeakoulu, Medialaboratorio, 2000. (in Finnish; mlab.uiah.fi/$\sim${}tosalmin/thesis/) †www ga00aTomiSalminen.
[31] Hee-Su Kim and Sung-Bae Cho. Application of interactive genetic algorithm to fashion design. Engineering Applications of Artificial Intelligence , 13(6):635–644, December 2000. * EBSCO 0519389 ga00bHee-SuKim.
[32] Mara Elizabeth Jones and Arvin Agah. Evolution of digital images. IEEE Transactions on Systems, Man,and Cybernetics-Part C: Applications and Reviews , 32(3):261–271, August 2002. ga02aMaraEJones.
[33] Pekka Ala-Siuru. AI in contemporary (interactive)art. In Pekka Ala-Siuru and Samuel Kaski, editors,STeP 2002 - Intelligence, The Art of Natural and Artificial, The 10th Finnish Artificial Intelligence Con- ference , pages 142–143, Oulu (Finland), 15.-17. December 2002. Finnish Artificial Intelligence Society.ga02aPAla-Siuru.
[34] Mahmoud S. Hamid, Neal R. Harvey, and Stephen Marshall. Genetic algorithm optimization of mul-tidimensional grayscale soft morphological filters with applications in film archive restoration. IEEE Transactions on Circuits and Systems for Video Technology , 13(5):406–416, May 2003. ga03aMSHamid ⇒http://ieeexplore.ieee.org/xpls/abs all.jsp?arnumber=1341312.
[35] Mahmoud S. Hamid and Stephen Marshall. FPGA realisation of the genetic algorithm for the design of
gray-scale soft morphological filters. In Proceedings of the 200 IEEE International Conference on , volume ?,pages 141–144. IEEE, Piscataway, NJ, ? 2003. ga03bMSHamid.
[36] Hung-Cheng Tsai and Jyh-Rong Chou. Automatic design support and image evaluation of two-colouredproducts using colour association and colour harmony scales and genetic algorithm. Computer-Aided Design ,39(?):818–828, ? 2007. ga07aHung-ChengTsai.
[37] Hung-Cheng Tsai, Chia-Young Hung, and Fei-Kung Hung. Automatic product color design using geneticsearching. In Andy Dong, Andrew Vande Moere, and John S. Gero, editors, Computer-Aided Architectural Design Futures (CAADFutures) 2007 , volume 10 of Architecture and Design , pages 513–524, ?, ? 2007.Springer-Verlag, Heidelberg. †www /Springer ga07bHung-ChengTsai ⇒ http://www.springerlink.com/
content/x216661l320u3674/.
8/10/2019 Bibliography of Genetic Algorithms in Arts and Music
http://slidepdf.com/reader/full/bibliography-of-genetic-algorithms-in-arts-and-music 35/49
Bibliography 31
[38] Ellie Baker and M. Seltzer. Evolving line drawings. In ?, editor, Proceedings of the Graphics Interface ’94,pages 91–100, Banff, Alta. (Canada), 18-20. May 1994. Canadian Information Processing Society, Toronto.* CCA 16288/95 ga94aEBaker.
[39] Shumeet Baluja, D. Pomerlau, and T. Jochem. Towards automated artificial evolution for computer-generated images. Connect. Science , 6(2-3):325–254, 1994. * CCA 16309/95 ga94bBaluja.
[40] Andrew B. Horner. Envelope matching with genetic algorithms. J. New Music Res. (Netherlands),
24(4):318–341, 1995. †CCA31940/96 ga95aHorner.[41] J. Ventrella. Disney meets Darwin – the evolution of funny animated figures. In Proceedings of the Computer
Animation ’95 , pages 35–43, Geneva (Switzerland), 19.-21. April 1995. IEEE Computer Society Press, LosAlamitos, CA. * [186] CCA 43415/95 ga95aVentrella.
[42] Michael Herdy. Evolution strategies with subjective selection. In Voigt et al. [187], pages 22–31. ga96aHerdy.
[43] Jarmo T. Alander. Indexed bibliography of genetic algorithms in art and music. Report 94-1-ART,University of Vaasa, Department of Information Technology and Production Economics, 1995. http:
//lipas.uwasa.fi/∼TAU/reports/report94-1/gaARTbib.pdf gaARTbib.
[44] William Latham. FormSynth: The rule-based evolution of complex forms from geometric primitives. InJ. Lansdown and R. A. Earnshaw, editors, Computers in Art, Design and Animation , page ? Springer-Verlag, Berlin, 1989. †citega:STodd92 ga:Latham89a.
[45] William Latham and Stephen Todd. Sculptures in the void. IBM Systems Journal , 28(4):?, ? 1989.†citega:STodd92 ga:Latham89b.
[46] William Latham and Stephen Todd. Sculptures in the void. New Scientist , ?(1701):?, 27. January 1990.†citega:STodd92 ga:Latham90a.
[47] William Latham, Karl Sims, Stephen Todd, and Michael Tolson. The applications of evolutionary andbiological processes to computer art and animation. In 20th Annual SIGGRAPH Computer Graphics Pro-ceedings , pages 389–390, Anaheim, CA, 1.-6. August 1993. ACM SIGGRAPH. ga:Latham93a.
[48] Karl Sims. Panspermia, 1990. ACM Sigraph Review (video tape) ga:Sims90.
[49] Karl Sims. Artificial evolution for computer graphics. Computer Graphics , 25(4):319–328, July 1991.ga:Sims91.
[50] Karl Sims. Interactive evolution of dynamical systems. In Varela and Bourgine [188], pages 171–178.ga:Sims92a.
[51] Karl Sims. Interactive evolution of equations for procedural models. In Proceedings of IMAGINA Conference ,page ?, Monte Carlo, 29.-30. January 1992. ? †[189] ga:Sims92b.
[52] Karl Sims. Interactive evolution of equations for procedural models. The Visual Computer , 9(?):466–476,1993. †Koza ga:Sims93a.
[53] Karl Sims. Evolving images. Paris, 1993. †Koza ga:Sims93b.
[54] Stephen Todd and William Latham. Mutator, a subjective human interface for evolution of computersculptures. UKSC report 248, IBM, 1990. †[57] ga:STodd90a.
[55] Stephen Todd and William Latham. Artificial life or surreal art? In Varela and Bourgine [188], pages504–513. ga:STodd91a.
[56] Stephen Todd, William Latham, and P. Hughes. Computer sculpture design and animation. Journal of Visualization and Computer Animation , 2(?):98–105, August 1991. †[57] ga:STodd91b.
[57] Stephen Todd and William Latham. Evolutionary Art and Computers . Academic Press, London, 1992. †ga:STodd92.
[58] William Latham. In the infinity of computer space there is a garden of unearthly delights, 1995. (CD-ROM)†IBM asiaa ga94aLatham.
[59] Ilpo Salonen. Kaarmemaiset sykkyrat pyorivat, hajoavat ja kulkevat itsensa lapi, tietokonetaiteilija WilliamLatham luo olioitaan evoluution saantojen avulla [Refers to works of computer artist William Latham].Helsingin Sanomat , ?(?):D3, 16. April 1994. (in Finnish) ga94aSalonen.
[60] Asko J. Makela. Alkavatko taideteoksetkin elaa? [Is art getting life?]. In Eero Hyvonen and Jouko Seppanen,editors, Keinoel¨ am¨ a – Artificial Life , pages 273–274, Helsinki (Finland), 12. May 1995. Finnish ArtificialIntelligence Society (FAIS), Espoo. (in Finnish) ga95aMakela.
[61] Marja-Leena Vepsalainen and William Latham. Tietokonetaide on monien ilmioiden leikkauspiste [computerart]. IBM asiaa , (1):1,20–21, February 1995. (in Finnish) ga95aVepsalainen.
8/10/2019 Bibliography of Genetic Algorithms in Arts and Music
http://slidepdf.com/reader/full/bibliography-of-genetic-algorithms-in-arts-and-music 36/49
32 Genetic algorithms in arts and music
[62] Vedran Vucic and Henrik Hautop Lund. Self-evolving arts— organisms versus fetishes. Muhely (The Hungarian Journal of Modern Art), 104(?):69–79, ? 1997. ga97aVVucic.
[63] Andrew B. Horner, James Beauchamp, and Lippold Haken. Methods for multiple wavetable synthesis of musical instrument tones. Journal of Audio Engineers Society , 41(5):336–356, May 1993. * EI 121094/93ga:Horner93a.
[64] Andrew Horner. Low peak amplitudes for wavetable synthesis. IEEE Transactions on Speech and Audio
Processing , 8(4):467–470, July 2000. ga00aAHorner.[65] Chin-Teng Lin, His-Wen Nein, and Jiing-Yuan Hwu. GA-based noisy speech recognition using two-
dimensional cepstrum. IEEE Transactions on Speech and Audio Processing , 8(6):664–675, November 2000.ga00aC-TLin.
[66] Erna L. Kentala, Jorma Laurikkala, Kati Viikki, Yrjo Auramo, Martti Juhola, and Ilmari V. Pyykko. Expe-riances of otoneurological expert system for vertigo. Scandinavian Audiology , 30(1):90–91, ? 2001. * www/Google ga01aELKentala ⇒ http://informahealthcare.com/doi/abs/10.1080/010503901300007182.
[67] E. A. Durant. Hearing Aid Fitting with Genetic Algorithms . PhD thesis, University of Michigan, 2002. †[?]ga02aEADurant.
[68] Andrew B. Horner. Wavetable matching synthesis of dynamic instruments with genetic algorithms. Journal of the Audio Engineering Society , 43(11):916–931, 1995. †EI M020278/95 ga95bHorner.
[69] B. G. T. Tan and S. M. Lim. Automatic parameter optimization for double frequency modulation synthesisusing the genetic annealing algorithm. Journal of the Audio Engineering Society , 44(1/2):3–15, 1996. †[180]ga96aBGTTan.
[70] B. T. G. Tan and S. M. Lim. Automated parameter optimization for douple frequency modulation synthesisusing the genetic annealing algorithm. Journal of the Audio Engineering Society , 44(1-2):3–15, 1996. †EIM063210/96 ga96aBTGTan.
[71] N.-M. Cheung and Andrew B. Horner. Group synthesis with genetic algorithms. Journal of the AudioEngineering Society , 44(3):130–147, 1996. †EEA68942/96 ga96bN-MCheung.
[72] San-Kuen Chan and Andrew B. Horner. Discrete summation synthesis of musical instrument tones us-ing genetic algorithms. Journal of the Audio Engineering Society , 44(7-8):581–592, 1996. †EEA109965ga96bS-KChan.
[73] Andrew B. Horner. Computation and memory tradeoffs with multiple wavetable interpolation. Journal of the Audio Engineering Society , 44(6):481–496, 1996. †EI M126494/96 ga96dHorner.
[74] J. Yuen and Andrew B. Horner. Hybrid sampling-wavetable synthesis with genetic algorithms. Journal of
the Audio Engineering Society , 45(5):316–330, 1997. †EEA78034/97 ga97aJYuen.
[75] Garrison W. Greenwood. Training partially recurrent neural networks using evolutionary strategies. IEEE Transactions on Speech & Audio Processing , 5(2):192–194, ? 1997. †Altavista/Greenwood ga97dGreenwood.
[76] Andrew Horner. Nested modulator and feedback FM matching of instrument tones. IEEE Transactions on Speech and Audio Processing , 6(4):398–409, July 1998. ga98aAHorner.
[77] J. Thomas Ngo. Global Optimization for Articulated Figures: Molecular Structure Prediction and Motion Synthesis for Animation . PhD thesis, Harvard University, Department of Biophysics, 1993. (http://www.
interval.com/∼\ngo/Pubs-wrld-byarea-960109.html) †[190] ga:Ngo93a.
[78] Tapio Takala, James Hahn, Larry Gritz, Joe Geigel, and Jong Won Lee. Using physically-based modelsand genetic algorithms for functional composition of sound signals, synchronized to animated motion. InProceedings of the 1993 International Computer Music Conference (ICMC93), pages 180–183, Waseda Uni-versity (Japan), 10.-15. September 1993. International Computer Music Association and Waseda University.
ga:Takala93a.[79] Hiromi Wakaki and Hitoshi Iba. Motion design of a 3D-CG avatar that uses humanoid animation. pages
195–201, 2002. ga02aHiromiWakaki.
[80] Anargyros Sarafopoulos and Bernard F. Buxton. Evolutionary algorithms in modeling and animation , pages29–62. Springer-Verlag, Berlin, 2003. †TKKpaa ga03aASarafopoulos.
[81] Alex Fukunaga, Joe Marks, and Tom Ngo. Automatic control of physically realistic animated figures usingevolutionary programming. In A. V. Sebald and Lawrence J. Fogel, editors, Proceedings of the Fourth Annual Conference on Evolutionary Programming (EP94), pages 76–83, San Diego, CA, 24.-26. February1994. World Scientific, Singapore. http://www.interval.com/ ngo/Pubs-wrld-byarea-960109.html †conf.progga94aFukunaga.
8/10/2019 Bibliography of Genetic Algorithms in Arts and Music
http://slidepdf.com/reader/full/bibliography-of-genetic-algorithms-in-arts-and-music 37/49
Bibliography 33
[82] Larry Gritz and James K. Hahn. Genetic programming for articulated figure motion. Journal of Visualiza-tion and Computer Animation , 6(3):129–142, July-September 1995. ga95aGritz.
[83] Heedong Ko, Jeong-Hwan Kim, and Jaihie Kim. Searching for facial expression by genetic algorithm. In?, editor, Virtual Environments ’95. Selected Papers of the Eurographics Workshops, Barcelona, Spain & Monte Carlo, Monaco, 1993 & Sept. 1995 , pages 87–98. Springer-Verlag, Wien, 1995. †CCA 55925/96ga95aHKo.
[84] J. Louchet, M. Boccara, D. Crochemore, and X. Provot. Building new tools for synthetic image animationby using evolutionary techniques. In ?, editor, Evolution Artificielle 95 (EA’95), page ?, Brest (France),4.-6. September 1995. Springer-Verlag, Berlin. †conf.prog ga95aLouchet.
[85] Tapio Takala. Keinoelamaa virtuaalitodellisuudessa – hyttysia ja muita otokoita [Artificial life in virtualreality – gnats and other little creatures]. In Eero Hyvonen and Jouko Seppanen, editors, Keinoel¨ am¨ a – Artificial Life , pages 260–264, Helsinki (Finland), 12. May 1995. Finnish Artificial Intelligence Society(FAIS), Espoo. (in Finnish) ga95aTakala.
[86] J. Louchet, X. Provot, and D. Crochemore. Evolutionary identification of cloth animation models. In?, editor, Proceedings of the Eurographics Workshop, volume ?, pages 44–54, Maastricht (Netherlands),2.-3. September 1995. Springer-Verlag, Berlin (Germany). †CCA66527/97 ga95bLouchet.
[87] Tomoharu Nagao, Takeshi Agui, and Hiroshi Nagahashi. An automatic GA-based construction of neuralnetworks for motion control of virtual life. Transactions of the Institute of Electronics, Information, and Communication Engineers D-II (Japan), J78D-2(7):1150–1152, 1995. †CCA77151/95 ga95bNagao.
[88] R. S. Stevens, R. D. Sewell, D. J. Lewis, R. D. Hamer, R. D. Williams, and S. Griffiths. Three-dimensionalcolour image and animation modelling for cal. Axis (UK), 2(1):25–29, 1995. †CCA 43418/95 ga95bStevens.
[89] Larry Gritz and James K. Hahn. Genetic programming evolution of controllers for 3-D character animation.In John R. Koza, Kalyanmoy Deb, Marco Dorico, David B. Fogel, Max Garson, Hitoshi Iba, and Rick L.Riolo, editors, Genetic Programming 1997: Proceedings of the Second Annual Conference , page ?, Stanford,CA, 13.-16. July 1997. Morgan Kaufmann, San Francisco, CA. †conf.prog ga97aGritz.
[90] D. Jackson and A. Fovargue. The use of animation to explain genetic algorithms. SIGCSE Bulletin ,29(1):243–247, 1997. †CCA43908/97 ga97aJackson.
[91] An Li and Kit Po Wong. Animating the evolution process of genetic algorithms. In B. McKay, X. Yao,C. S. Newton, J.-H. Kim, and T. Furuhashi, editors, Simulated Evolution and Learning, Second Asia-Pacific Conference on Simulated Evolution and Learning, SEAL’98 , volume LNAI of 1585 , pages 341–348, Canberra(Australia), November 1999. Springer-Verlag Berlin Heidelberg. * www /Springer ga99aALi.
[92] T. Polichroniadis and N. Dodgson. Motion blending using a classifier system. In Proceedings of the 7th International Conference in Central Europe on Computer Graphics , volume 1, pages 225–232, Plzen(Czech Republic), 8.-12. February 1999. Univ. West Bohemia, Plzen (Czech Republic). †CCA73416/99ga99aPolichro.
[93] Jan T. Kim and Kurt Stuber. Patterns of cluster formation and evolutionary activity in evolving L-systems.In ?, editor, Self-organization and life, from simple rules to global complexity, Proceedings of the Second European Conference on Artificial Life , pages 547–563, Brussels (Belgium), 24.-26. May 1993. MIT Press,Cambridge, MA. ga:JTKim93a.
[94] Toshiyuki Masui. Graphic object layout with interactive genetic algorithms. In Proceedings of the 1992 IEEE Workshop on Visual Languages , pages 74–80, Seattle, WA, 15.-18. September 1992. IEEE ComputerSociety Press, Los Alamitos, CA. * CCA 61297/93 ga:Masui92a.
[95] Victor S. Johnston and M. Franklin. Is beauty in the eye of the beholder. Ethology and Sociobiology ,14(3):183–199, May 1993. * ISI ga:VSJohnston93a.
[96] Christian Jacob. Illustrating Evolutionary Computation with Mathematica . Morgan Kaufmann Publishers,San Francisco, CA, 2001. (translation of [135]) †TKKpaa ga01aCJacob.
[97] Shinn-Ying Ho and Yeong-Chinq Chen. An efficient evolutionary algorithm for accurate polygonal approx-imation. Pattern Recognition , 34(12):2305–2317, December 2001. ga01aShinn-YingHo.
[98] Xuejie Qin and Yee-Hong Yang. Estimating parameters for procedural texturing by genetic algorithms.Graphical Models , 64(?):19–39, ? 2002. ga02aXuejieQin.
[99] Alfonso Ortega, Abdellatif Abu Dalhoum, and Manuel Alfonseca. Grammatical evolution to design frac-tal curves with a given dimension. IBM Journal of Research & Development , 47(4):483–492, July 2003.ga03aAOrtega.
8/10/2019 Bibliography of Genetic Algorithms in Arts and Music
http://slidepdf.com/reader/full/bibliography-of-genetic-algorithms-in-arts-and-music 38/49
34 Genetic algorithms in arts and music
[100] Marc Ebner. Evolutionary design of objects using scene graphs. In Conor Ryan, Terence Soule, MaartenKeijzer, Edward Tsang, Riccardo Poli, and Ernesto Costa, editors, Genetic programming, 6th European Conference, EuroGP 2003 Proceedings , volume 2610 of Lecture Notes in Computer Science , pages 47–58,Essex (UK), 14.-16. April 2003. Springer-Verlag, Berlin. ga03aMarcEbner.
[101] Zsolt Janko, Evgeny Lomonosov, and Dmitry Chetverikov. Building photorealistic models using data fusion.In ?, editor, Proceedings of the Hungarian Conference on Computer Graphics and Geometry , pages 37–42,Budapest (Hungary), ? 2005. ? ga05aZsoltJanko.
[102] Dmitry Chetverikov, Zsolt Janko, Evgeny Lomonosov, and Aniko Ekart. Creating photorealistic models bydata fusion with genetic algorithms. In Mike Nachtegael, Dietrich Van der Weken, Etienne E. Kerre, andWilfried Philips, editors, Soft Computing in Image Processing, Recent Advances , volume 210 of Studies in Fuzziness and Soft Computing , pages 239–264, ?, ? 2006. Springer. ga06aDChetverikov.
[103] Donald H. House, Alethea Bair, and Colin Ware. An approach to the perceptual optimization of com-plex visualizations. IEEE Transaction on Visualization and Computer Graphics , 12(4):509–521, ? 2006.ga06aDonaldHHouse ⇒ http://ccom.unh.edu/publications/House 06 VCG Perceptual optimization of
complex visualizations.pdf.
[104] Zsolt Janko. Photorealistic 3D Models of Real-World Objects . PhD thesis, Eotvos Lorand University,Computer and Automation Research Institute, 2006. * www /Google ga06aZsoltJanko.
[105] Karl G. Baum, Marıa Helguera, Evan Schmidt, Kimberly Rafferty, and Andrzej Krol. Evaluation of geneticalgorithm-generated multivariate color tables for the visualization of multimodal medical fused data sets.In Proceedings of the 2008 IEEE Nuclear Science Symposium Conference , volume ?, pages 4355–4360, ?, ?2008. IEEE, Piscataway, NJ. ga08aKarlGBaum.
[106] Miguel Frade, F. Fernandez de Vega, and Carlos Cotta. Modelling video games’ landscapes by means of genetic terrain programming - a new approach for improving users’ experience. In M. Giacobini et al , editor,Proceedings of the EvoWorkshops 2008 , volume 4974 of Lecture Notes in Computer Science , pages 485–490,?, ? 2008. Springer-Verlag, Heidelberg. ga08aMiguelFrade.
[107] Christian Jacob. Genetic L-system programming. In Yuval Davidor, Hans-Paul Schwefel, and ReinhardManner, editors, Parallel Problem Solving from Nature – PPSN III , volume 866 of Lecture Notes in Computer Science , page ?, Jerusalem (Israel), 9.-14. October 1994. Springer-Verlag, Berlin. †conf. prog. ga94aJacob.
[108] Corey Kosak, Joe Marks, and Stuart Shieber. Automating the layout of network diagrams with specifiedvisual organization. IEEE Transactions on Systems, Man, and Cybernetics , 24(3):440–454, 1994. †[191]ga94aKosak.
[109] Brigitta Lange and Markus Beyer. Rayvolution: an evolutionary ray tracing algorithm. In Proceedings of the Photorealistic Rendering Techniques , pages 136–144, 430, Darmstadt, Germany, 13.-15. June 1994.Springer-Verlag, Berlin (Germany). ga94aLange.
[110] Markus Beyer. Approximation der rendering equation durch evolutionare algorithmen. Master’s thesis,Technische Hochschule Darmstadt, 1994. †www /Google ga94aMBeyer.
[111] Masashi Yamada, Hidenori Itoh, Hirohisa Seki, and Rahmat Budiarto. A cat’s cradle string diagramdisplay method based on a genetic algorithm. Forma , 9(1):11–28, ? 1994. †www /MathRev98i:57020ga94aMYamada.
[112] Markus Beyer and Brigitta Lange. Rayvolution: An evolutionary ray tracing algorithm. In ?, editor,Proceedings of the Fifth Eurographics Workshop on Rendering , pages 137–146, Darmstadt (Germany), June1994. ? †www /Google ga94bMBeyer.
[113] Karl Sims. Evolving virtual creatures. In ?, editor, Proceedings of the ACM SIGGRAPH’94: Computer Graphics , pages 15–22, Orlando, FL, July 1994. SIGGRAPH, ACM? †Bounsaythip ga94bSims.
[114] Toshiyuki Masui. Evolutionary learning of graph layout constraints from examples. In Proceedings of the
Seventh Annual Symposium on User Interface Software and Technology , pages 103–108, Marina del Rey,CA, USA, 2.-4. November 1994. ACM, New York, NY. * CCA58146/96 ga94bToshiyukiMasui.
[115] Markus Beyer and Brigitta Lange. Simulation of global illumination: An evolutionary approach. Technicalreport MPI issue 241 (KI Workshop: Genetic Algorithms Within the Framework of Evolutionary Compu-tation, Max-Planck Intitut fur Informatik, Saarbrucken (Germany), 1994. †www /Google ga94cMBeyer.
[116] David John Nettleton and Roberto Garigliano. Evolving fractals. Journal of Computers and Graphics ,19(5):779–782, 1995. †CCA13835/95 ga95aDJNettleton.
[117] Christian Jacob. MathEvolvica – Simulierte Evolution von Entwicklungsprogrammen der Natur . PhDthesis, University of Erlanger-Nurnberg, Institut fur mathematische Maschinen und Datenverarbeitung,1995. †[126] ga95aJacob.
8/10/2019 Bibliography of Genetic Algorithms in Arts and Music
http://slidepdf.com/reader/full/bibliography-of-genetic-algorithms-in-arts-and-music 39/49
Bibliography 35
[118] Brigitta Lange. An evolution model for integration problems. In Pearson et al. [192], pages 206–209.ga95aLange.
[119] Jeanine Graf. Interactive evolutionary algorithms in design. In Pearson et al. [192], pages 227–230.ga95bGraf.
[120] A. Hirose, H. Furuta, and T. Nakatani. Application of genetic algorithm to design of artificial ground.In Proceedings of ISUMA - NAFIPS ‘95 The Third International Symposium on Uncertainty Modeling
and Analysis and Annual Conference of the North American Fuzzy Information Processing Society , pages101–104, College Park, MD, 17.-20. September 1995. IEEE Computer Society Press, Los Alamitos, CA.†CCA88626/95 ga95bHirose.
[121] A. O. Rodriguez and A. R. Suarez. Automatic graph drawing by genetic search. In ?, editor, Proceedings of the 11th ISPE/IFAC International Conference on CAD/CAM, Robotics and Factories of the Future CARS and FOF95 , volume 2, pages 982–987, Pereira, Colombia, 28.-30. August 1995. Univ. Tecnologica de Pereira,Pereira (Colombia). †CCA30725/96 ga95bRodriguez.
[122] R. D. Schraft, J. Neugebauer, T. Flaig, and R. Dainghaus. A fuzzy controlled rendering system for virtualreality systems optimised by genetic algorithms. In Proceedings of the Virtual Environments’95 , pages22–32, Barcelona, Spain & Monte Carlo, Monaco, September 1995. Springer-Verlag, Wien (Austria).†CCA57244/96 ga95bSchraft.
[123] C. Thornborrow and A. Hobden. Genetic programming for easy 3D texture generation. In Proceedings of the Eurographics Conference , pages 107–116, Loughborough (UK), 28.-30. March 1995. Eurographics UK,Abingdon, UK. †CCA83406/95 ga95bThornborrow.
[124] Christian Jacob. Genetic L-system programming: breeding and evolving artificial flowers with Mathematica.In Proceedings of the First International Mathematica Symposium , pages 215–222, Southampton (England),16.-20. July 1995. Comput. Mech. Publications, Southampton (UK). †CCA33976/97 ga95cJacob.
[125] K. Aoki, Hideyuki Takagi, and N. Fujimura. Interactive GA-based design support system for lighting designin computer graphics. In Proceedings of the 4th International Conference on Soft Computing , volume 2,pages 533–536, Fukuoka, Japan, 30. Sep - 5. Oct 1996. World Scientific, Singapore. †CCA57722/97ga96aAoki.
[126] Christian Jacob. Evolving evolution programs: Genetic programming and L-systems. In Koza et al. [193],page ? †conf.prog ga96aJacob.
[127] Jinghuan Lu and Kikou Fujimura. Shape transformation in space-time. The Visual Computer , 12(9):455–473, ? 1996. * www /Springer ga96aJLu.
[128] Hideo Saito and M. Kimura. Shape modeling of multiple objects from shading images using genetic al-
gorithms. In Proceedings of the 1996 IEEE International Conference on Systems, Man and Cybernetics ,volume 4, pages 2463–2468, Beijing, China, 14.-17. October 1996. IEEE, New York, NY. †Saito/wwwEEA25675/97 ga96aSaito.
[129] Sean Clark. Organic art. Internet Today , (17):16–19, March 1996. ga96aSClark.
[130] C. Traxler and M. Gervautz. Using genetic algorithms to improve the visual quality of fractal plants gen-erated with CSG-PL-systems. In Proceedings of the Fourth International Conference in Central Europe on Computer Graphics and Virtual Worlds , volume 2, pages 367–376, Pizen, Czech Republic, 12.-16. February1996. Univ. West Bohemia, Pizen, Czech Republic. †CCA57291/96 ga96aTraxler.
[131] Yang Xuan and Liang Dequn. An improved genetic algorithm of solving IFS code of fractal image. InProceedings of the 3rd International Conference on Signal Processing , volume 2, pages 1405–1408, Beijing(China), 14.-18. October 1996. IEEE, New York, NY. †CCA70904/97 ga96aYangXuan.
[132] Peter J. Angeline. Evolving fractal movies. In Koza et al. [193], page ? †conf.prog ga96bAngeline.
[133] Hideo Saito and Makoto Kimura. Superquadrics modeling of multiple objects from shading images usinggenetic algorithms. In Proceedings of the 1996 IEEE 22nd International Conference on Industrial Electron-ics, Control, and Instrumentation (IECON), volume 3, pages 1589–1593, Taipei (Taiwan), 5.-10. August1996. IEEE Computer Society Press, Los Alamitos, CA. ga96bHSaito.
[134] Christian Jacob. Evolution programs evolved. In Voigt et al. [187], pages 42–51. ga96bJacob.
[135] Christian Jacob. Principia Evolvica, Simulierte Evolution mit Mathematica . dpunkt-Verlag, Heidelberg(Germany), 2001. (In English as [96]) †[96] ga97aCJacob.
[136] Eduardo Bustillo. A neuro-evolutionary unbiased global illumination algorithm. In J.Dorsey and Ph.Slusallek, editors, Renderin Techniques ’97, Proceedings of the Eurographics Workshop, pages 263–274, St.Etienne (France), 16.-18. June 1997. Springer-Verlag, Berlin. ga97aEBustillo.
8/10/2019 Bibliography of Genetic Algorithms in Arts and Music
http://slidepdf.com/reader/full/bibliography-of-genetic-algorithms-in-arts-and-music 40/49
36 Genetic algorithms in arts and music
[137] I. Kelly. A two-stage evolutionary model for the computer-aided design of colour combinations. Digit. Creat.(UK), 8(3-4):106–112, 1997. †CCA14195/98 ga97aIKelly.
[138] K. Aoki and H. Takagi. 3-D CG lighting with an interactive GA. In Proceedings of the 1997 First Interna-tional Conference on Knowledge-Based Intelligent Electronic Systems , volume 1, pages 296–301, Adelaide,SA (Australia), 21.-23. May 1997. IEEE, New York, NY. †CCA94744/97 ga97aKAoki.
[139] Michael Kenward. Where to fish for neural nets. Scientific Computing World , ?(32):66, October 1997.
ga97aKenward.[140] H. Tanahashi, N. Murakami, and Kenji Yamamoto. Recovery of superquadric primitives from a range image
using GA. In ?, editor, Rapid Product Development Technologies , volume SPIE-2910, pages 28–33, Boston,MA, 18.-19. November 1997. SPIE - Int. Soc. Opt. Eng. †EEA76091/97 ga97aTanahash.
[141] O. V. Verner, R. L. Wainwright, and D. A. Schoenefeld. Placing text labels on maps and diagrams usinggenetic algorithms with masking. INFORMS J. Comput., 9(3):266–275, Summer 1997. * CCA 8868/98ga97aVerner.
[142] A. A. Brice and W. R. Johns. Optimization of flowsheet drawing layout using a genetic algorithm. Computers in Chemical Engineering , 22(1-2):47–67, 1998. †CCA16976/98 ga98aAABrice.
[143] A. Rosete and A. Ochoa. Genetic graph drawing. In Proceedings of the Thirteeth International Conference on Applications of Artificial Intelligence in Engineering , pages 37–40, Galway (Ireland), 7.-9. July 1998.Computational Mechanics Publications, Ltd., Southhampton, UK. †CCA66851/99 ga98aARosete.
[144] Gabriela Ochoa. On genetic algorithms and Lindenmayer systems. In Agoston E. Eiben, Thomas Back,Marc Schoenauer, and Hans-Paul Schwefel, editors, Parallel Problem Solving from Nature - PPSN V, 5th International Conference , volume LNCS of 1498 , pages 335–344, Amsterdam (The Netherlands), September1998. Springer-Verlag Berlin Heidelberg. * www /Springer ga98aGOchoa.
[145] Y. G. Zhang, M. Sugisaka, and X. J. Li. Lifelike artificial trees based on growth iterated function system.Appl. Math. Comput. (USA), 91(1):3–8, 1998. †CCA58957/98 ga98aYGZhang.
[146] A. Aoki and H. Takagi. Interactive GA-based design support system for lighting design in 3-D computergraphics. Transactions of the Institute of Electronics, Information, and Communication Engineers D-II (Japan), J81D-II(7):1601–1608, 1998. In Japanese †CCA80327/98 ga98bAAoki.
[147] Christian Jacob. Evolution and coevolution of developmental programs. Computer Physics Communications ,121-122(xxi-xxxvi):46–50, 1999. (Proceedings of the Europhysics Conference on Computational Physics,CCP 1998) ga99aCJacob.
[148] Steven C. Gustafson, Andrew W. Learn, Gordon R. Little, and John S. Loomis. Evolutionary data visu-alization. In Nickolas L. Faust and Steve Kessinger, editors, Modeling, Simulation, and Visualization for Real and Virtual Environments , volume SPIE-3694, pages 83–92, ?, July 1999. The International Societyfor Optical Engineering. * www/SPIE Web ga99aSCGustafson.
[149] C. G. Schaefer Jr. Morphogenesis of path plan sequences through genetic synthesis of l-system produc-tions. In Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 , volume 1, pages 358–365,Washington D.C., 6.-9. July 1999. IEEE, Piscataway, NJ. †CCA79537/99 ga99aSchaefer.
[150] D. Terzopoulos. Artificial life for computer graphics. Communications of the ACM , 42(8):32–43, 1999.†CCA82827/99 ga99aTerzopou.
[151] Y. Li, J. F. Yu, and H. C. Yang. Optimized connection of rational surface-based on genetic algorithms.In H. P. Chen and J. H. Gu, editors, Proceedings of the 6th International Conference on Computer Aided Design & Computer Graphics , volume ?, pages 1025–1028, Shanghai, China, 1.-3.December 1999. Wen HuiPublishers, Shanghai. †P89847 ga99aYLi.
[152] Andrew B. Horner and David E. Goldberg. Genetic algorithms and computer-assisted music composition.In Richard K. Belew and Lashon B. Booker, editors, Proceedings of the Fourth International Conference on Genetic Algorithms , pages 437–441, San Diego, 13.-16. July 1991. Morgan Kaufmann Publishers. also as[153] ga:Goldberg91d.
[153] Andrew B. Horner and David E. Goldberg. Genetic algorithms and computer-assisted music composition.IlliGAL Report 91002, University of Illinois at Urbana-Champaign, 1991. also as [152] ga:Goldberg91dd.
[154] Andrew B. Horner and David E. Goldberg. Genetic algorithms and computer-assisted music composition.In B. Alphonce and B. Pennycook, editors, ICMC - International Computer Music Conference: Proceedings ,pages 437–441, Montreal, ? 1991. †P57038 ga:Goldberg91ddd.
8/10/2019 Bibliography of Genetic Algorithms in Arts and Music
http://slidepdf.com/reader/full/bibliography-of-genetic-algorithms-in-arts-and-music 41/49
Bibliography 37
[155] K. Ricanek, II, Abdollah Homaifar, and G. Lebby. Genetic algorithm composes music. In Proceedings of the SSST’93 The Twenty-Fifth Southeastern Symposium on System Theory , pages 223–227, Tuscaloosa, AL,7.-9. March 1993. IEEE Computer Society Press, Los Alamitos, CA. * CCA 36080/95 ga:Homaifar93a.
[156] Andrew B. Horner, James Beauchamp, and Lippold Haken. Machine tongues XVI. genetic algorithms andtheir application to FM matching synthesis. Computer Music Journal , 17(4):17–29, Winter 1993. * EIM061921/94 CCA 44928/94 ga:Horner93b.
[157] P. M. Gibson and J. A. Byrne. Neurogen
, music composition using genetic algorithms and cooperatingneural networks. In Proceedings of the Second International Conference on Artificial Neural Networks ,volume Conf. Pub. No. 349, pages 309–313, London (UK), 18.-20. November 1991. IEE. * EI A098639/92ga:PMGibson91a.
[158] Alfonso Ortega de la Puente, Rafael Sanhez Alfonso, and Manuel Alfonseca Moreno. Automatic compositionof music by means of grammatical evolution. In Proceedings of the 2002 Conference on APL: array processing languages: lore, problems, and applications , pages 148–155, Madrid (Spain), ? 2002. ACM Press, New York.(also as [160]) †ACM /www ga02aAOPuente.
[159] Sang M. Soak, Seok C. Chang, Taehwan Shin, and Byung-Ha Ahn. Music recognition system using ART-1 and GA. In David P. Casasent and Tein-Hsin Chao, editors, Optical Pattern Recognition XIII , volumeSPIE-4734, pages 172–180, ?, March 2002. The International Society for Optical Engineering. * www/SPIEWeb ga02aSMSoak.
[160] Alfonso Ortega de la Puente, Rafael Sanhez Alfonso, and Manuel Alfonseca Moreno. Automatic composition
of music by means of grammatical evolution. volume 32, page ?, 2002. (also as [158]) †ACM /wwwga02bAOPuente.
[161] David Andrew Birchfield. Evolving intelligent musical materials . PhD thesis, Columbia University, 2003.* www /DAI-A 64/04 ga03aDABirchfield.
[162] J. McCormack. Evolving sonic ecosystems. Kybernetes , 32(1-2):184–202, 2003. * ISI ga03aJMcCormack.
[163] Gustavo Miguel Jorge dos Reis and Francisco Fernandez de Vega. A novel approach to automatic musictranscription using electronic synthesis and genetic algorithms. In ?, editor, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2007), volume ?, pages 2915–2922, London (UK), July2007. ACM. ga07aGMJdosReis ⇒ http://portal.acm.org/citation.cfm?id=1274054.
[164] Gustavo Miguel Jorge dos Reis and Francisco Fernandez de Vega. Electronic synthesis using genetic al-gorithms for automatic music transcription. In ?, editor, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2007), volume ?, pages 1959–1966, London (UK), July 2007. ACM.ga07bGMJdosReis ⇒ http://portal.acm.org/citation.cfm?id=1277348.
[165] Gustavo Reis, Francisco Fernandez, and Aniıbal Ferreira. Evolutionary algorithms and automatic tran-scription of music. In ?, editor, Proceedings of the 14th International Conference on Genetic and Evolu-tionary Computation (GECCO Companion ’12), pages 477–484, ?, ? 2012. ACM. ga12aGustavoReis ⇒http://dl.acm.org/citation.cfm?id=2330857.
[166] Damon Horowitz. Generating rhytms with genetic algorithms. In Proceedings of the Tweftth National Conference on Artificial Intelligence , volume 2, page 1459, Seattle, WA, 31. July-4. August 1994. AAAIPress / The MIT Press. * ga94aHorowitz.
[167] John A. Biles. GenJam: A genetic algorithm for generating jazz solos. In ?, editor, Proceedings of the International Computer Music Conference , page 131, ?, ? 1994. ? † ga94aJohnABiles ⇒ https:
//www.zotero.org/diegomaranan/items/itemKey/ASHJTZ4C.
[168] Pauli Laine and Mika Kuuskankare. Genetic algorithms in musical style oriented generation. In ICEC’94[194], pages 858–862. ga94aLaine.
[169] Ryan A. McIntyre. Bach in a box: The evolution of four part baroque harmony using the genetic algorithm.In ICEC’94 [194], pages 852–857. ga94aMcIntyre.
[170] Andrew B. Horner and L. Ayers. Harmonisation of musical progression with genetic algorithms. In ?, editor,Proceedings of the 1995 Computer Music Conference , pages 483–484, ?, ? 1995. ? †[180] ga95aABHorner.
[171] M. A. Hall. Selection of attributed for modeling Bach chorales by a genetic algorithm. In Proceedings of the Second New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems , pages 182–185, Dunedin (New Zealand), 20.-23. November 1995. IEEE Comput. Soc. Press, LosAlamitos, CA (USA). †CCA42982/96 ga95bMAHall.
[172] Couro Kane and Marc Schoenauer. A drum shape optimisation by genetic algorithms. Complexity Inter-national , 2(?):?, April 1995. ga95dKane.
8/10/2019 Bibliography of Genetic Algorithms in Arts and Music
http://slidepdf.com/reader/full/bibliography-of-genetic-algorithms-in-arts-and-music 42/49
38 Genetic algorithms in arts and music
[173] J. Putnam. A grammar based genetic programming technique applied to music generation. In ?, editor,Proceedings of the Fifth Annual Conference on Evolutionary Programming , pages 363–368, San Diego, CA(USA), 29. February- 3. March 1996. MIT Press, Cambridge, MA. †CCA107920/97 ga96aJPutnam.
[174] K. Thywissen. GeNotator: An environment for investigating the application of genetic algorithms in com-puter assisted composition. In ?, editor, Proceedings of the 1996 Computer Music Conference , pages 274–277,?, ? 1996. ? †[180] ga96aThywissen.
[175] J. A. Biles, Peter G. Anderson, and Laura W. Loggi. Neural network fitness functions for a musical IGA. InKevin Warwick, editor, Proceedings of the International ICSC Symposia on Intelligent Industrial Automation and Soft Computing , pages B39–44, Reading, UK, 26.-28. March 1996. Int. Comput. Sci. Conventions, Millet,Alta. †CCA85988/96 ga96bBiles.
[176] Andrew B. Horner and L. Ayers. Common tone adaptive tuning using genetic algorithms. The Journal of the Acoustical Society of America , 100(1):630–640, July 1996. ga96fABHorner.
[177] John A. Biles. Genjam: An interactive genetic algorithm jazz improviser. The Journal of the Acoustical Society of America , 102(5):3181, November 1997. †NASA ADS ga97aJABiles.
[178] J. Polito, Jason M. Daida, and Tommaso F. Bersano-Begey. Musica ex machina: composing 16th-centurycounterpoint with genetic programming and symbiosis. In Proceedings of the 6th International Conference,Evolutionary Programming , pages 113–123, Indianapolis, IN, 13.-16. April 1997. Springer-Verlag, Berlin(Germany). †CCA78226/97 ga97aPolito.
[179] A. R. Burton and T. Vladimirova. Genetic algorithm utilising neural network fitness evaluation for musical
composition. In George D. Smith and Nigel C. Steele, editors, Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms , pages 219–223, Norwich, UK, 2.-4. April 1997.ga97bARBurton.
[180] Anthony Richard Burton. A hybrid neuro-genetic pattern evolution system applied to musical composition .PhD thesis, University of Surrey, School of Electronic Engineering, 1998. (http://www.ee.surrey.ac.uk/
Personal/A.Burton/work.html) ga98aARBurton.
[181] Geraint Wiggins, George Papadopoulos, Somnuk Phon-Amnuaisuk, and Andrew Tuson. Evolutionary meth-ods for musical composition. In ?, editor, Proceedings of the CASYS98 Workshop on Anticipation, Music & Cognition , page ?, Liege, ? 1998. ? ga98aGWiggins.
[182] George Papadopoulos and Geraint Wiggins. A genetic algorithm for the generation of jazz melodies. InPasi Koikkalainen and Seppo Puuronen, editors, Human and Artificial Information Processing, Proceedings of the 8th Finnish Artificial Intelligence Conference , pages 241–250, Jyvaskyla (Finland), 7.-9. September1998. University of Jyvaskyla — Finnish Artificial Intelligence Society. ga98aPapadopoulos.
[183] Hannu Saukkosaari. Optimizing additive synthesis parameters with genetic algorithms and self-organizingmaps. In Pasi Koikkalainen and Seppo Puuronen, editors, Human and Artificial Information Processing,Proceedings of the 8th Finnish Artificial Intelligence Conference , pages 234–240, Jyvaskyla (Finland), 7.-9. September 1998. University of Jyvaskyla — Finnish Artificial Intelligence Society. ga98aSaukkosaari.
[184] Toshiya Yoshida. Method and device for generating musical sound waveform, 1998. (JP patent no. 10149167.Issued June 2 1998) * fi.espacenet.com ga98aToshiyaYoshida.
[185] A. Pazos, A Santos del Riego, J. Dorado, and J. J. Romero Caldalda. Genetic music compositor. In Pro-ceedings of the 1999 Congress on Evolutionary Computation-CEC99 , volume 2, pages 885–890, Washington,DC, 6.-9. July 1999. IEEE, Piscataway, NJ. †CCA85895/99 ga99aAPazos.
[186] Jarmo T. Alander, editor. Proceedings of the Third Nordic Workshop on Genetic Algorithms and their Applications (3NWGA), Helsinki (Finland), 18.-22. August 1997. Finnish Artificial Intelligence Society(FAIS). (ftp://ftp.uwasa.fics/3NWGA/*.ps.Z) ga97NWGA.
[187] Daniel S. Weile and Eric Michielssen. Genetic algorithm optimization applied to electromagnetics - a review.IEEE Transactions on Antennas and Propagation , 45(3):343–353, March 1997. (89 refs) ga97aWeile.
[188] Hans-Michael Voigt, Werner Ebeling, Ingo Rechenberg, and Hans-Paul Schwefel, editors. Parallel Problem Solving from Nature – PPSN IV , volume 1141 of Lecture Notes in Computer Science , Berlin (Germany),22.-26. September 1996. Springer-Verlag, Berlin. ga96PPSN4.
[189] Francisco J. Varela and Paul Bourgine, editors. Toward a Practice of Autonomous System: Proceedings of the First European Conference on Artificial Life , Paris, 11.-13. December 1991. MIT Press, Cambridge,MA. ga:ECAL91.
[190] John R. Koza. Genetic Programming: On Programming Computers by Means of Natural Selection and Genetics . The MIT Press, Cambridge, MA, 1992. ga:Koza92book.
8/10/2019 Bibliography of Genetic Algorithms in Arts and Music
http://slidepdf.com/reader/full/bibliography-of-genetic-algorithms-in-arts-and-music 43/49
University of Vaasa, Finland 39
[191] Catherine Bounsaythip and Jarmo T. Alander. Genetic algorithms in image processing - a review. InAlander [195], pages 173–192. (ftp://ftp.uwasa.fics/3NWGA/Bounsaythip.ps.Z) ga97aBounsaythip.
[192] Cees H. M. van Kemenade. Modeling elitist genetic algorithms with a finite population. In Alander [195],pages 3–16. (ftp://ftp.uwasa.fics/3NWGA/Kemenade.ps.Z) ga97aKemenade.
[193] D. W. Pearson, N. C. Steele, and R. F. Albrecht, editors. Artificial Neural Nets and Genetic Algorithms ,Ales (France), 19.-21. April 1995. Springer-Verlag, Wien New York. ga95ICANNGA.
[194] John R. Koza, David E. Goldberg, David B. Fogel, and Rick L. Riolo, editors. Proceedings of the GP-96 Conference , Stanford, CA, 28.-31. July 1996. MIT Press, Cambridge, MA. †prog ga96GP.
[195] Proceedings of the First IEEE Conference on Evolutionary Computation , Orlando, FL, 27.-29. June 1994.IEEE, New York, NY. ga94ICCIEC.
Notations
†(ref) = the bibliography item does not belong to my collection of genetic papers.(ref) = citation source code. ACM = ACM Guide to Computing Literature, EEA = Electrical & Elec-tronics Abstracts, BA = Biological Abstracts, CCA = Computers & Control Abstracts, CTI = CurrentTechnology Index, EI = The Engineering Index (A = Annual, M = Monthly), DAI = Dissertation Ab-stracts International, P = Index to Scientific & Technical Proceedings, PA = Physics Abstracts, PubMed= National Library of Medicine, BackBib = Thomas Back’s unpublished bibliography, Fogel/Bib = DavidFogel’s EA bibliography, etc* = only abstract seen.? = data of this field is missing (BiBTeX-format).
The last field in each reference item in Teletype font is the BiBTEXkey of the corresponding reference.
8/10/2019 Bibliography of Genetic Algorithms in Arts and Music
http://slidepdf.com/reader/full/bibliography-of-genetic-algorithms-in-arts-and-music 44/49
40 Genetic algorithms in arts and music
8/10/2019 Bibliography of Genetic Algorithms in Arts and Music
http://slidepdf.com/reader/full/bibliography-of-genetic-algorithms-in-arts-and-music 45/49
Appendix A
Abbreviations
The following other abbreviations were used to compress the titles of articles in the permutation titleindex:
AI = Artificial Intelligence
Alg. = Algorithm(s)AL = Artificial LifeANN(s) = Artificial Neural Net(work)(s)Appl. = Application(s), AppliedAppr. = Approach(es)Cntr. = Control, Controlled,
= Controlling, Controller(s)Coll. = ColloquiumComb. = CombinatorialConf. = ConferenceCS(s) = Classifier System(s)Distr. = DistributedEng. = EngineeringEP = Evolutionary Programming
ES = Evolutionsstrategie(n),= Evolution(ary) strategies
Evol. = Evolution, EvolutionaryExS(s) = Expert System(s)FF(s) = Fitness Function(s)GA(s) = Genetic Algorithm(s)Gen. = Genetic(s), Genetical(ly)GP = Genetic ProgrammingIdent. = IdentificationImpl. = Implementation(s)
Int. = International
ImPr = Image ProcessingJSS = Job Shop SchedulingML = Machine LearningNat. = NaturalNN(s) = Neural Net(work)(s)Opt. = Optimization, Optimal,
= Optimizer(s), OptimierungOR = Operation(s) ResearchPar. = Parallel, ParallelismPerf. = PerformancePop. = Population(s), Populational(ly)Proc. = ProceedingsProg. = Programming, Program(s), ProgrammedProb. = Problem(s)
QAP = Quadratic Assignment ProblemRep. = Representation(s), Representational(ly)SA = Simulated AnnealingSch. = Scheduling, Schedule(s)Sel. = Selection, SelectionismSymp. = SymposiumSyst. = System(s)Tech. = Technical, TechnologyTSP = Travel(l)ing Salesman Problem
41
8/10/2019 Bibliography of Genetic Algorithms in Arts and Music
http://slidepdf.com/reader/full/bibliography-of-genetic-algorithms-in-arts-and-music 46/49
Appendix B
Bibliography entry formats
This documentation was prepared with LATEX and reproduced from camera-ready copy supplied by the editor. The oneswho are familiar with BibTeX may have noticed that the references are printed using abbrv bibliography style and haveno difficulties in interpreting the entries. For those not so familiar with BibTeX are given the following formats of themost common entry types. The optional fields are enclosed by ”[ ]” in the format description. Unknown fields are shownby ”?”. † after the entry means that neither the article nor the abstract of the article was available for reviewing and sothe reference entry and/or its indexing may be more or less incomplete.
Book: Author(s), Title , Publisher, Publisher’s address, year.
Example
John H. Holland. Adaptation in Natural and Artificial Systems . The University of Michigan Press,Ann Arbor, 1975.
Journal article: Author(s), Title, Journal , volume(number): first page – last page, [month,] year.
Example
David E. Goldberg. Computer-aided gas pipeline operation using genetic algorithms and rule learning.Part I: Genetic algorithms in pipeline optimization. Engineering with Computers , 3(?):35–45, 1987.† .
Note: the number of the journal unknown, the article has not been seen.Proceedings article: Author(s), Title, editor(s) of the proceedings, Title of Proceedings, [volume,] pages, location of theconference, date of the conference, publisher of the proceedings, publisher’s address.
ExampleJohn R. Koza. Hierarchical genetic algorithms operating on populations of computer programs. InN. S. Sridharan, editor, Eleventh International Joint Conference on Artificial Intelligence (IJCAI-89),pages 768–774, Detroit, MI, 20.-25. August 1989. Morgan Kaufmann, Palo Alto, CA. † .
Technical report: Author(s), Title, type and number, institute, year.
Example
Thomas Back, Frank Hoffmeister, and Hans-Paul Schwefel. Applications of evolutionary algorithms.Technical Report SYS-2/92, University of Dortmund, Department of Computer Science, 1992.
42
8/10/2019 Bibliography of Genetic Algorithms in Arts and Music
http://slidepdf.com/reader/full/bibliography-of-genetic-algorithms-in-arts-and-music 47/49
Vaasa GA Bibliography 43
Vaasa Genetic Algorithm Bibliography
Search & Optimise
Main features:
• Over 20,000 references to published papers
• by over 20,000 researchers.
• Available as over 70 special bibliographies online:
http://lipas.uwasa.fi/~TAU/reports/report94-1/ga*bib.pdf files.
• Covers all sciences and engineering fields, from basic theory to applica-
tions.
• Several indexes and statistical summaries.
• See what problems evolution can solve for you!
Global optimisation and search heuristics called genetic algorithm mimics evolution in nature usingrecombination and selection from a set of solution trials called population. One of the most prominentattractive features of genetic algorithms from the practical point of view of software techniques is theirsimplicity, which makes them easy to implement and tailor to solve practical search and optimisationproblems.
In spite of the seemingly simple processing, the genetic algorithms are good at solving some problemsthat are known to be hard. The simplicity, generality, flexibility, parallelism, and the good problem solvingcapability have made genetic algorithm very popular among various disciplines desperately searchingmethods to solve difficult optimisation problems.
—————Observe that our server has also a selection of our papers on genetic algorithms and other compuationaltopics. See our bibliographies or file ftp.uwasa.fi/cs/README for further details.
8/10/2019 Bibliography of Genetic Algorithms in Arts and Music
http://slidepdf.com/reader/full/bibliography-of-genetic-algorithms-in-arts-and-music 48/49
44 Vaasa GA Bibliography
file # refs updated contents
ga90bib.ps.Z GA in 1990...
.
.....
.
..ga02bib.ps.Z 557 GA in 2002gaACOUSTICSbib.pdf 190 2009/08/17 GA in acousticsgaAIbib.pdf 2566 2013/06/14 GA in artificial intelligence
gaAERObib.pdf 911 2014/05/06 GA in aerospacegaAGRObib.pdf 405 2012/08/01 GA in agriculturegaALIFEbib.pdf 184 2014/05/06 GA in artificial lifegaARTbib.pdf 174 2014/05/06 GA in art and musicgaAUSbib.pdf 720 2013/05/14 GA in Australia and New ZealandgaBASICSbib.pdf 1177 2014/04/28 Basics of GAgaBIObib.pdf 1635 2014/05/06 GA in biosciences including medicinegaCADbib.pdf 1407 2012/07/30 GA in Computer Aided DesigngaCHEMbib.pdf 938 2009/07/24 GA in chemical sciences ; previously in gaCHEMPHYSbib.ps.ZgaCHEMPHYSbib.ps.Z 2277 GA in chemistry and physics; divided into gaCHEMbib.ps.Z and gaPHYSbib.ps.Z gaCIVILbib.pdf 1068 2009/01/07 GA in civil, structural, and mechanical engineeringgaCODEbib.pdf 377 2008/03/20 GA codinggaCOEVObib.pdf 232 2008/09/18 co- and differential evolution GAgaCONTROLbib.pdf 1881 2012/08/08 GA in control and process engineeringgaCSbib.pdf 1453 2008/03/20 GA in comp. sci. (incl. databases, /mining, software testing and GP)gaEARLYbib.pdf 723 2014/04/28 GA in early years (upto 1989)
gaEAST-EURObib.ps.Z 679 2003/07/09 GA in the Eastern EuropegaECObib.pdf 1569 2012/07/16 GA in economics and financegaECOLbib.pdf 177 2012/07/16 GA in ecology and biodiversitygaELMAbib.pdf 574 2012/07/20 GA in electromagneticsgaESbib.pdf 464 2008/08/13 Evolution strategiesgaFAR-EASTbib.ps.Z 1556 2011/12/29 GA in the Far East (excl. Japan)gaFEMbib.pdf 90 2014/05/06 GA & FEMgaFINbib.pdf 891 2013/05/22 GA in FinlandgaFPGAbib.pdf 435 2013/11/18 GA & FPGAgaFRAbib.ps.Z 540 2011/12/29 GA in FrancegaFTPbib.ps.Z 1353 2003/07/09 GA papers available via web (ftp and www)gaFUZZYbib.pdf 1521 2012/09/21 GA and fuzzy logicgaGAMEbib.pdf 140 2014/05/06 GA and gamesgaGEObib.pdf 458 2014/05/06 GA in geosciencesgaGERbib.ps.Z 1586 2004/09/22 GA in Germany, Austria, and SwitzerlandgaGPbib.pdf 1006 2012/07/30 genetic programming
gaIMPLEbib.pdf 1500 2012/07/30 implementations of GAgaINDIAbib.ps.Z 276 2003/05/23 GA in IndiagaINVERSEbib.pdf 291 2010/01/08 GA in inverse problemsgaIREGbib.pdf 204 2013/10/28 image registrationgaISbib.pdf 87 2009/08/17 immune systemsgaJAPANbib.ps.Z 2475 2013/05/14 GA in JapangaLCSbib.pdf 211 2012/08/08 Learning Classifier SystemsgaLASERbib.pdf 58 2009/07/31 GA and lasersgaLATINbib.ps.Z 649 2003/07/09 GA in Latin America, Portugal & SpaingaLOGISTICSbib.pdf 741 2014/05/06 GA in logistics (incl. TSP)gaMANUbib.pdf GA in manufacturinggaMATHbib.pdf 846 2009/07/27 GA in mathematicsgaMEDICINEbib.pdf 739 2012/08/01 GA in medicinegaMEDITERbib.ps.Z 1810 2003/07/09 GA in the MediterraneangaMICRObib.pdf 83 2008/03/31 GA in microscopy & microsystemsgaMILbib.pdf 113 2009/08/17 GA in military applicationsgaMLbib.pdf 1231 2012/08/08 GA in machine learninggaMSEbib.pdf 575 2013/08/15 GA in materialsgaNANObib.pdf 117 2012/07/17 GA in nanotechnologygaNIRbib.pdf 267 2013/11/18 GA in NIRS (spectroscopy)gaNNbib.pdf 1883 2012/06/28 GA in neural networksgaNORDICbib.pdf 1125 2013/11/18 GA in Nordic countriesgaOPTICSbib.pdf 2168 2014/04/28 GA in optics and image processinggaOPTIMIbib.pdf 923 2003/07/09 GA and optimization (only a few refs)gaORbib.pdf 1704 2012/07/30 GA in operations research
...table continues on the next page...
8/10/2019 Bibliography of Genetic Algorithms in Arts and Music
http://slidepdf.com/reader/full/bibliography-of-genetic-algorithms-in-arts-and-music 49/49
Vaasa GA Bibliography 45
file # refs updated contents
gaPARAbib.pdf 833 2012/07/30 Parallel and distributed GAgaPARETObib.pdf 469 2009/03/24 Pareto optimizationgaPATENTbib.pdf 462 2009/07/27 GA patentsgaPATTERNbib.pdf 1654 2012/09/21 GA in pattern recognition incl. LCS
gaPHYSbib.pdf 2313 2008/04/07 GA in physical sciences ; previously in gaCHEMPHYSbib.ps.ZgaPIEZObib.pdf 57 2012/07/18 GA & piezogaPOWERbib.pdf 976 2012/06/28 GA in power engineeringgaPROTEINbib.pdf 491 2008/03/12 GA in protein researchgaPSObib.pdf 92 2013/08/15 Particle Swarm OptimisationgaQCbib.pdf 547 2011/03/09 quantum computinggaREMOTEbib.pdf 302 2012/07/20 GA in remote sensinggaROBOTbib.pdf 775 2009/07/27 GA in roboticsgaSAbib.pdf 331 2009/07/24 GA and simulated annealinggaSCHEDULINGbib.pdf 862 2011/12/29 GA in schedulinggaSELECTIONbib.ps.Z 295 2009/07/27 Selection in GAsgaSIGNALbib.pdf 2587 2012/07/27 GA in signal and image processinggaSIMULAbib.pdf 1037 2009/07/24 GA in simulationgaTELEbib.pdf 840 2009/07/27 GA in telecomgaTHEORYbib.pdf 2654 2012/09/17 Theory and analysis of GAgaTHESESbib.pdf 578 2009/01/07 PhD etc theses
gaVAASAbib.pdf 284 2010/08/17 GA in VaasagaVLSIbib.pdf 799 2012/07/16 GA in electronics, VLSI design and testinggaUKbib.ps.Z 1998 2008/05/22 GA in United KingdomgaXbib.ps.Z 129 2013/08/15 GA & X-rays
Table B.1: Indexed genetic algorithm special bibliographies available online in directoryhttp://lipas.uwasa.fi/~TAU/reports/report94-1. New updates only as .pdf files.
top related