arab academy for science, technology & maritime...
TRANSCRIPT
----------------------~~~~~------------------~ ~ ,~ ~t""7' \1{t/tr
1~"A\'of'"
ARAB ACADEMY FOR SCIENCE, TECHNOLOGY
AND MARITIME TRANSPORT
(AASTMT)
College of Computing and Information Technology
Department of Information Systems
AN EVOLUTIONARY-IMMUNE APPROACH FOR
UNIVERSITY COURSE TIMETABLING
By
YOSRA AHMED AWAD
Egypt
A thesis submitted to the College of Computing and Information Technology
AASTMT in partial
fulfillment of the requirements for the award of the degree of
MASTER of SCIENCE
In
INFORMATION SYSTEMS
Supervisors:
Prof. Ahmed Rida Dawood
Head of Business and Information
System department
College of Management & Technology
AASTMT
2011
Prof. Amr Badr
Department of Computer Science
Faculty of Computers and Information
Cairo University
ARAB ACADEMY FOR SCIENCE, TECHNOLOGY
AND MARITIME TRANSPORT
(AASTMT)
College of Computing and Information Technology
Department of Information Systems
AN EVOLUTIONARY-IMMUNE APPROACH FOR
UNIVERSITY COURSE TIMETABLING
By
YOSRA AHMED A WAD
Egypt
A thesis submitted to the College of Computing and Information Technology
AASTMT in partial
fulfiUment of the requirements for the award of the degree of
MASTER of SCIENCE
In
INFORMATION SYSTEMS
Su perviso rs
Prof. Amr Badr Prof. Ahmed Rida Dawood
Department of Computer Science Faculty of
Computers and Information
Head of Business and Information System
department
Cairo University
2011
College of Management & Technology
AASTMT
4"l.~, ~ I,u,Syria. Latakia branch
P O.Box 869 Latakia Tei (+96341) 210045 Fax:(+96341) 453977
• ,f, ~···1I·1'~ -.'11--10 JJ!.CI":;/t~A .1.. '.< <.J.J,,:·II ;.~ .:::,\_;..-"' < r"-:-\'I\ ~~i!.'~~~.~>~)' Arab Academy for Science, Technology & Maritime Transport
DECLARATION
We clarify that we have read the present work and that in our opinion it is fully adequate in scope and quality as dissertation towards the partial fulfillment of the Master Degree requirements in
Specialization :Information System
College of Computing and Information Technology (AASTMT)
November- 2011
Thesis Title
An Evolutionary-Immune Approach for University Course Timetabling
Submitted By
Yosra Ahmed Awad
Supervisor (5):
Name: Prof. Dr. Ahmed Rida Dawood
Position: puter Science, Arab AcadelJ'Y for Science and Technology
~
Name: Prof. Dr. Amr Anwar Badr
Position:
sild'P.imri~3~=I:::::::::::::::=~ ................. .
Examiners:
Name: Prof. Dr. Mustafa Samy Mahmoud Mustafa
Position: Professor of Co uter Science, Helwan University J
. / ......... ',-,/ Signature: ..................... , ............. ,. ....................... .
Name: Prof. Dr. madan ,v,oawad Mohamed Ahmed
Position: Professor of Computer Science, Arab Academy for Science and Technology
.s"I~'Io:'~~ Ganoub AI Wadi branch
Aswan-Sadat Road- P.O.Box 11 Aswon Tel: (+2m7) 23328451 2332843
Fax:( +2m7) 2332842
23 Doctor Sobky st Tel: (+202) 37481593/33365491
Fax:( +202) 33365492
www.aastmt.org
;;~~I~~-;;~1.iaI1
Cairo· Mis, El Gedida branch
PO.Box 2033 - Elhorna EI Mashir Ismail St. -behind Sheraton Bldg.
Tei (+202) 22685616/22685615 Fax ( +202) 22685892
~.;JI~I_~~'it Alexandria - Main Campus
P.O.Box 1029 - Miami Miami Tel: (+203) 5565429 / 5481163
Fax ( +203) 5487786/5506042 Abukir Tel: (+203) 5622366/5622388
Fax:( +203) 5610950
Certification
I certify that all the material in this thesis that is not my own work has been
identified, and that no material is included for which a degree has previously
been conferred on me.
The contents on this thesis reflect my own personal views, and are not
necessarily endorsed by the Academy.
Student name: YosraAhmed Awad
Signiture:
Published Work:
The work in the thesis is published as follows:
1. Y. Awad, A.Badr, A.Dawood, 2011, An Evolutionary Immune Approach for
University Course Timetabling, The International Journal of Computer Science and
Network Security (IJCSNS), Vol. 11, issue 2.
ii
i.~ti.),..
(32) ~~t
III
Acknowledgment
lowe the completion of this thesis to many people who have supported me either
technically or morally. Firstly, I would like to thank my dear family especially my dad for
his encouragement. Also, I would like to express my appreciation to my fiancee for his
patience and support. And last but not least, I would like to extend my gratitude to my
supervising professors for their continuous support and guidance. I dedicate this work to my
late mother, God have mercy on her soul.
iv
Abstract
The university course timetabling problem (UCTP) is a combinatorial NP-complete
problem that has been subject to research since the early 1960's. Numerous solution
techniques have been applied to the timetabling problem ever since. This thesis begins by
investigating the nature and characteristics of the UCTP and generally reviewing the
various solution techniques used in solving it.
The broad objective of this thesis can be summarized as: experimenting with the
effectiveness of Artificial Immune Systems (AIS) in solving UCTP. The essence of this
thesis focuses on fonnulating an immune-inspired algorithm, namely the Clonal Selection
Algorithm 1 (CSAl) and testing its ability in solving the UCTP against the Genetic
algorithm (GA). An Immune-Genetic algorithm (IGA) was also created, which combines
the crossover operator borrowed from the genetic algorithm with immune-inspired
concepts.
Also, experimenting with the effects of changing the selection and re-selection
schemes of the algorithms motivated the creation of a second version of CSAl, that is
CSA2 and three more versions of IGA namely: IGAI, IGA2 and IGA3. All the devised
algorithms were contrasted in their perfonnances against the GA. Enhancements were
applied to the mutation operator of the formulated algorithms by introducing a "move
factor". As a means of improving the results attained by the algorithms, local search
consisting of variable neighborhoods was incorporated into each of them.
The algorithms were tested with over two problem instances, with varying complexities and
the results demonstrate the effectiveness of the created algorithms in solving the UCTP.
Keywords:
Clonal Selection Algorithms; Genetic algorithm; Hybrid algorithm; University course
timetabling problem.
v
Table of Contents
Contents Page
Certification ................................................................................. , ..
Published Work ................................ , ....... , ............................ '" .. ....... ii
Acknowledgement .... , .. , .......................... " '" ............. " . . .. . .. .. .... .. . . . . . .. iv
Abstract......................................................................................... v
List of Tables.............................................................................. .... xi
List of Figures.............. ................. ........ ... .... .................... ...... .......... xii
Nomenclatures............ .............................................................. ....... xiv
Chapter 1: Introduction
1.1
1.2
1.3
1.4
1.5
1.6
Overview ........................................................................... .
The Problem ...................................................................... ,
The motivation ................................................................... .
Research objectives .............................................................. .
Thesis Approach .................................................................. .
Thesis Organization .............................................................. .
Chapter 2: An Overview of Solution Techniques for the Timetabling Problem
2
4
5
6
7
8
2.1 The UCTP......................................................................... 11
2.2 Solution techniques for the UCTP .......................................... '" 13
2.2.1 Classical techniques ............................ , .... " .... " .. , . .. ... .. . . . . . . . .. 13
2.2.1.1 Direct heuristics .. " ... , ...... '" ........... " . .. . . . .. . . . . . . . . . . . .. . . .. . . . .. . . . . . . 14
2.2.1.2 Graph coloring ................................................................... , 15
2.2.1.3 Network flow ................. '" ... , ., .. , . . ... . . . ... .. . .. . .. ... . . . . . . . .... . .. . . . . 17
VI
2.2.1.4 Integer Programming ............... , ................ " .. , . . . . . . . . . . . . . . . . . . . . . . . 19
2.2.1.5 Limitations of classical techniques .... , " ........... " ......... " ........... ,. 20
2.2.2 Non-Classical techniques ... , ... , ........ , ...... , ........... " .......... , . . . . . .. 21
2.2.2.1 Constraint-based Approaches.................................................. 22
2.2.2.2 Simulated Annealing ..... " . " ............. , ............ '" ................... " 25
2.2.2.3 Tabu Search......................................................... ............... 27
2.2.2.4 Hyper- heuristics .............................................................. '" 29
2.2.2.5 Case-based Reasoning........................................................... 30
2.2.2.6 Swarm Intelligence............................................................... 31
Chapter 3: An overview of Artificial Immune Systems and its Algorithms
3.1 Introduction and Origins of AIS................................... ............. 34
3.2
3.2.1
3.2.2
3.2.3
3.3
3.4
3.4.1
3.4.2
3.4.3
3.4.4
3.4.5
3.4.6
3.4.7
3.4.8
Biological inspiration for AIS: The Immune System ....................... .
The innate immune system ............................................. '" ..... .
The adaptive Immune System .................................................. .
The Concept of Shape-spaces .................................................. .
Learning and memory in the immune system ................................ .
AIS theories and Algorithms ................................................... .
The Negative Selection Principle ............................................... .
The Negative Selection Algorithms (NSA) ................................. ..
The Positive Selection Theory .................................................. .
The Positive Selection Algorithms (PSA) .................................... .
The Immune Network Theory .................................................. .
The Immune Network Algorithms ............................................ ..
The Danger theory principles .................................................. .
The Clonal Selection Principle ................................................ .
vii
35
35
36
38
39
40
40
41
42
42
43
44
44
45
3.4.9
3.4.9.1
Clonal Selection Algorithms (CSA) ......................................... .
The Clonal Algorithm CLONALG ........................................... .
46
48
3.4.9.2 The Artificial Immune Recognition Systems (AIRS) ..................... 52
3.4.9.3 The B-cell algorithm (BCA) ................................................... 54
3.4.9.4 The immunological Algorithm (IA) .......................................... 55
3.4.9.5 The Multi-objective Immune System Algorithm (MISA).................. 55
3.5 AIS application to the timetabling problem......... ..................... ...... 56
Chapter 4: Evolutionary Computation and the Genetic Algorithm
4.1 Evolutionary computation....................................................... 60
4.2 Biological Evolution ......... " ........... " ....................... '" ... '" ., . .. 60
4.3 Evolutionary Algorithms ........................ ,. '" ................. '" ........ 61
4.4 Evolutionary Algorithm techniques............................................ 62
4.5 The Canonical Genetic Algorithm .......................................... '" 62
4.5.1 Representation in the GA................................................... ..... 64
4.5.2 Selection Schemes............................................................... 64
4.5.2.1 Affects of Selection schemes on algorithm performance. . . . . . . . . . . . . . .. . .. 68
4.5.2.2 Tradeoff between exploration and exploitation.............................. 69
4.5.3 Crossover ....................................................................... '" 69
4.5.4 Mutation........................................................................... 70
4.5.5 The Fitness Function....... ... ...... ..... ............................... ......... 71
4.5.6 Replacement or Re-selection......... ......................... ............... ... 72
4.5.7 Stopping conditions. ........... ........... .... ...... ................ .......... ... 73
4.6 Genetic algorithms and local search in timetabling.......................... 74
viii
Chapter 5: The Proposed Framework for applying AlS to the UCTP
5.1
5.1.1
5.1.2
5.1.3
5.2
5.3
5.4
5.5
5.6
5.7
5.7.1
5.7.2
5.7.3
5.7.4
5.7.5
5.7.6
5.8
5.8.1
5.9
5.9.1
5.9.2
5.9.3
5.9.4
5.10
Representation ................................................................. '"
Problem Classes .................................................................. .
Description of the Problem Instances ......................................... .
Solution Representation ......................................................... .
AffmitylFitness Functions ....................... " . " ................... " ..... .
The Genetic Algorithm ........................................................ '"
Incorportaing LS into the GA ................................................... .
The Local Search Algorithm ................................................... .
The Matching Algorithm ........................................................ .
The Devised Algorithms ...... " ... " ......... " . " ...... " ..................... .
The Clonal Selection Algorithml (CSAI) ................................... .
The Clonal Selection Algorithm 2 (CSA2) ................................... .
The Immuno-Genetic Algorithm (lGA) ...................................... .
The Immuno-Genetic Algorithm (IGA I) .................................... .
The Immuno-Genetic Algorithm (lGA2) ................................... ..
The Immuno-Genetic Algorithm (IGA3) .................................... .
Additional Algorithm Parameters ............................................. .
Discussion of Algorithm parameters and effects ............................... ..
Results .............................................................................. ..
Test 1 criterion: fitness/affinity against generations ........................... ..
Test 2 criterion: Number of soft constraint violations .......................... .
Test 3 criterion: Best solution attained ........................................... .
Test 4 criterion: Average convergence time ...................................... .
Discussion of Results ............................................................... ..
ix
77
78
79
81
82
83
84
85
88
88
93
96
97
99
100
101
103
105
109
109
112
116
117
119
Chapter 6: Conclusion and future work
6.1
6.2
6.3
Conclusion ......................................................................... .
Summary of Contributions .......................................................... .
Future Work .... , .. " ........... , ...... " ................................. , ........ .
125
126
127
References............... ................ .............. .............. ........................... 128
Appendix A: Code................... ................. ................................... .... 141
x
List of Tables
Table No.
3.1
5.1
5.2
Title
Parameters ofCLONALG and their values ................................. .
Parameter values for the three instance classes ............................. .
Common command line parameters .. , ... " . " ............................... .
5.3 Differences in the selection and re-selection schemes between
Page
51
78
91
algorithms .... , ............. , ............................ , ....................... " 102
5.4
5.5
5.6
5.7
5.8
5.9
6.1
Additional command line parameters, and their presence in algorithms ...................... , .. , ............................................. . Relationship between the mutation rate a. and the decay Pd ... ...... '" '" .
Algorithms with lowest value for number of SCV s at each pointer ..... .
Algorithms with highest value for number of SCV s at each pointer ..... .
Ranking of the algorithms by minimum number ofSCVs and the time
of their attainment. ............................................................. .
The average convergence time of the algorithms for the smalll and
mediuml instances ............................................................. .
The results of CLONALG applied to the smalll problem instance ...... .
103
107
115
115
116
118
123
6.2 The results of the devised algorithms applied to the small 1 problem
instance .................... ; . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
xi
List of Figures
Figure Caption No.
2.1 . A constraint graph .............. " ............... " .................... " ....... .
3.1 The Activation and recognition mechanisms of the immune
3.2
3.3
3.4
3.5
3.6
3.7
3.8
3.9
3.10
4.1
5.1
systems .......................... : ............................................... .
The shape-space model. ....................................................... .
Monitoring process for detecting changes in strings ..................... .
The Positive Selection Algorithm .......................................... ..
Flowchart of a typical Immune network algorithm ....................... ..
An illustration of the Danger theory ....................................... ..
Flowchart of CLONALG for Pattern Recognition ........................ .
Flowchart of CLONALG for optimization ................................. ..
The Artificial Immune Recognition System (AIRS) ...................... .
The B-cell Algorithm .......................................................... .
The canonical genetic algorithm ............................................ ..
A matrix of student attendance and its representation in the problem
Page
15
37
38
41
43
44
45
48
50
53
54
63
instance..... ...................... .... ................. ............... ...... ....... 80
5.2
5.3
5.4
5.5
5.6
5.7
5.8
5.9
5.10
5.11
5.12
5.13
5.14
A room/feature matrix and its representation in the problem instance ...
An event/feature matrix and its representation in the problem instance.
The Genetic Algorithm ... , ................... " ...................... , .... '" .. ,
The local Search ............................................................... ..
A flowchart of the local search ............................................... .
An illustration emphasizing the difference between re-selection and
replacement policy .............. " .. , . " ...................................... ..
The Clonal Selection Algorithm 1 (CSA 1) ................................. .
The Clonal Selection Algorithm 2 (CSA2) ................................. ..
The Immuno-Genetic Algorithm (lGA) .................................... ..
The Immuno-Genetic Algorithm 1 (IGAl) ................................. .
The Immuno-Genetic Algorithm 2 (IGA2) ................................ ..
The Immuno-Genetic Algorithm 3 (IGA3) ................................ ..
A summary of the algorithm steps for the devised algorithms ........... .
xii
80
81
84
85
87
89 93
96
97
99
100
101
104
List of Figures (Cont'd)
Figure no. Caption
5.15 A comparison of the affinity of hybrid algorithms for the smalll
Page
intance ........................................................................... " 110
5.16 A comparison of the fitness/affinity between GA, CSAl, CSA2 and
IGA 1 for the smalll instance................................................... 11 0
5.17 A comparison of the affinity of hybrid algorithms for the medium 1
instance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . 111
A comparison of the fitness/affinity between GA, CSAl, CSA2 and 5.18
IGA for the medium 1 instance ................................................ . 111
A boxplot of the number ofSCV for all trials on the smalll 5.19
instance. '" ....................................................................... . 113
5.20 A boxplot of the number of SCV for all trials on the mediuml
instance ............................. " . . . . . . . . . . . . . . .. . . . . . . . . . . .... . .. . . . . . . . . . . ... 113
xiii
Nomenclatures
ACS:
aiNET:
AIRS:
AIS:
APC:
ATS:
ARB:
BCA:
CBR:
CLONALG:
CSA:
CSP:
CSP:
DTA:
EA
GA:
Hev:
IA:
IGA:
INA:
IP:
LQ:
MISA:
MMAS:
MN:
NSA:
NSACT:
Opt-IA:
Ant Colony System
Artificial Immune NETwork
Artificial Immune Recognition System
Artificial Immune System
Antigen-Presenting Cell
Adaptive Tabu Search
Artificial Recognition Ball
B-cell Algorithm
Case-based Reasoning
CLONal ALGorithm
Clonal Selection Algorithm
Clonal Selection Principle
Constraint Satisfaction Problem
Danger theory Algorithm
Evolutionary Algorithm
Genetic Algorithm
Hard Constraint Violation
Immunological Algorithm
Immune Genetic Algorithm
Immune Network Algorithm
Integer Programming
Lower Quarti Ie
Multi-objective Immune System Algorithm
Max-Min Ant System
Metaheuristics Network
Negative Selection Algorithm
Negative Selection Algorithm for Course Timetabling
Optimization Immune Algorithm
xiv
Nomenclatures (Cont'd)
PAMP:
PRR:
PSA:
pso: SA:
SCV:
SI:
SIA:
swo: TS:
TS:
UCTP:
UQ:
Pathogen Associated Molecular Pattern
Pattern Recognition Receptor
Positive Selection Algorithm
Particle Swann Optimization
Simulated Annealing
Soft Constraint Violation
Swarm Intelligence
Simple Immune Algorithm
Squeaky Wheel Optimization
Tabu Search
Timeslot
University Course Timetabling Problem
Upper Quartile
xv
1. Abdennadher S., Marte M., University Course Timetabling using Constraint
Handling Rules, Journal of Applied Artificial Intelligence, vo1.l4, issue 4, 2000,
pp.311-326.
2. Abdullah S., Burke E., McCollum B., A hybrid evolutionary approach to the
university course timetabling problem, in Proceedings of the IEEE Congress on
Evolutionary Computation (CEC), 2007, pp. 1764-1768.
3. Abdullah S., Turabieh H., McCollum B., Burke E., An Investigation of a Genetic
Algorithm and Sequential Local Search Approach for Curriculum-based Course
Timetabling Problems, Multidisciplinary International Conference on Scheduling:
Theory and Applications (MISTA 2009), Dublin, Ireland, 2009.
4. Aickelin U., Bentley P., Cayzer S., Kim J., McLeod J., Danger theory: The link
between ais and ids, in Proceedings of the Second International Conference on
Artificial Immune Systems, 2003, pp.147-155.
5. Aickelin U., Cayzer S., The danger theory and its application to artificial immune
systems, Proceeding of the 1 st Int. Conf. on Artificial Immune Systems, 2002,
pp.141-148.
6. AI-Enezi J.R., Abood M.F., Alsharhan S., Artificial immune systems- models,
algorithms and applications, IJRRAS, vol. 3, issue 2,2010.
7. Alvarez-Valdes R., Crespo E., Tamarit J.M., Tabu Search: An Efficient
Metaheuristic for University Organization Problems, Revista Investigacion
operacional, vo1.22, issue 2, 200 1.
8. Antonisse H.J., Keller K.S., Genetic operators for high-level knowledge
representations, Proceedings 2nd Int. Conf. on Genetic Algorithms, Cambridge,
MA, 1987. pp. 6~ 76.
130
9. Arntzen H., Lekketangen A., A tabu search heuristic for a university timetabling
problem, The 5th Metaheuristics International Conference (MIC2003), 2003.
10. Atmar W., Notes on the Simulation of Evolution, IEEE Trans. Neural Networks,
Vol. 5, 1994, pp.130--147.
11. Bai R., Burke E. K., Kendall G., McCullum B., A Simulated Annealing Hyper
heuristic for University Course Timetabling Problem, in Proceedings of the 6th
International Conference on the Practice and Theory of Automated Timetabling
(pATAn,2006.
12. Baker J., Reducing Bias and Inefficiency in the Selection Algorithm, Proceedings
of the Second International Conference on Genetic Algorithms and their
Application, Hillsdale, New Jersey, 1987, pp.14-21.
13. Baklr M.A, Aksop c., A 0-1 Integer Programming Aapproach to a University
Timetabling Problem, Hacettepe Journal of Mathematics and Statistic vol. 37, issue
1,2008, pp.41-55.
14. Blidde T., Thiele L., A comparison of selection schemes used in genetic
algorithms, Technical Report No. 11, Gloriastrasse 35, CH-8092 Zurich: Swiss
Federal Institute of Technology (ETH), Computer Engineering and Communication
Networks Lab (TIK), 1995.
15. Brownlee J., Clonal Selection Algorithms, Technical Report 070209A, Complex
Intelligent Systems Laboratory, Centre for Information Technology Research,
Faculty of Information Communication Technology, Swinburne University of
Technology, 2007.
16. Burke E. K., MacCarthy B. L., Petrovic S., Qu R., Multiple-Retrieval Case-Based
Reasoning for Course Timetabling Problems, Journal of Operations Research
Society, vol. 57, issue 2, 2006 b, pp.I48-162.
131
17. Burke E.K., Kendall G., Soubeiga E., 2003, A Tabu-Search Hyperheuristic for
Timetabling and Rostering, Journal of Heuristics, vol. 9, pp.451-4 70.
18. Burke E.K., McCollum B., Meisels A., Petrovic S., Qu R., A graph-based hyper
heuristic for educational timetabling problems. European Journal of Operational
Research, vol. 176, 2007, pp. 177-192.
19. Burke E.K., Petrovic S., Recent Research Directions in Automated Timetabling,
European Journal of Operational Research - EJOR, vo1.140, issue 2,2002, pp. 266-
280.
20. Burke E.K., Petrovic S., Qu R., Based Heuristic Selection for Timetabling
Problems, Journal of Scheduling, vol. 9, issue 2, 2006 a, pp. 115-132.
21. Busetti F., Simulated Annealing Overview, Report, available from:
www.geocities.comlfrancorbusettilsaweb.pdf. 2003.
22. Caldwell C. K., Graph Theory Tutorials, available from:
http://www.utm.eduldepartments/mathlgraphl, accessed February 2010.
23. Carrasco M.P., Pato M.V., A Multiobjective Genetic Algorithm for the
Classffeacher Timetabling Problem, The Practice and Theory of Automated
Timetabling Ill: Selected Papers from the 3rd International Conference on the
Practice and Theory of Automated Timetabling, Springer Lecture Notes in
Computer Science, vo1.2070, 2001, pp.3-17.
24. Carter M.W., Laporte G., Recent developments in practical examination
timetabling, Practice and Theory of Automated Timetabling, Burke E. and Carter
M. (eds.), Springer-Verlag, Lecture Notes in Computer Science, vol. 1153, 1995,
pp.3-21.
132
25. Chu S.C., Chen Y.T., Ho I.H., Timetable scheduling using particle swarm
optimization, in Proceedings of the International Conference on Innovative
Computing, Information and Control (ICICIC), Beijing, vol.3, 2006, pp.324-327.
26. Coello C.A.C., Cortes N.C, An approach to solve multiobjective optimization
problems based on an Artificial Immune System, 1 st International Conference on
Artificial Immune Systems (ICARIS), University of Kent, Catenbury, UK, 2002,
pp.212-221.
27. Cooper T.B. and Kingston 1.H., The complexity of timetable construction
problems, The Practice and Theory of Automated Timetabling, Burke E.K. and
Ross P. (eds.): Selected Papers the First International Conference, Springer Lecture
Notes in Computer Science, vo1.l153, 1996, pp.283-295.
28. Cutello V., Narzisi G., Nicosia G., Pavone M., Clonal selection algorithms: A
Comparative Case Study Using Effective Mutation Potentials, in Proceedings of
4th International Conference on Artificial Immune Systems, LNCS, vol. 3627,
2005, pp. 13-28.
29. Cutello V., Nicosia G., Multiple learning using immune algorithms, in proceedings
of 4th International Conference on Recent Advances in Soft Computing (RASC),
Nottingham, UK, 2002, pp. 102-107.
30. Daskalaki S., Birbas T. ,Housos E., An integer programming formulation for a
case study in university timetabling, European Journal of Operational Research,
vol. 153,2004, pp. 117-l35.
31. Datta D., Deb K., Fonseca C.M., Multi-Objective Evolutionary Algorithm for
University Class Timetabling Problem, Studies in Computational Intelligence (SCI)
vo1.49, 2007, pp.197-236.
32. De Castro L.N., Fundamentals of Natural Computing: Basic Concepts, Algorithms
and Applications, CRC Press LLC, 2006.
133
33. De Castro L.N., Von Zuben F.J, Learning and Optimization Using the Clonal
Selection Principle, IEEE Transactions on Evolutionary Computation, Special Issue
on Artificial Immune Systems, vol. 6, issue 3, 2002, pp. 239-251.
34. De Castro L.N., Von Zuben F.J., An Evolutionary immune Network for Data
Clustering, Proceedings of the IEEE Brazilian Symposium on Artificial Neural
Networks, 2000, pp.84-89.
35. De Castro N., Timmis J.I., Artificial Immune Systems: A New Computational
Intelligence Approach, Springer, 2002.
36. De Jong K., An Analysis of the Behavior of a Class of Genetic Adaptive Systems,
Doctoral Thesis, Department of Computer and Communication Sciences,
University of Michigan, 1975.
37. De Werra D., An introduction to timetabling. European Journal of Operational
Research, vol. 19, 1985, pp. 151-162.
38. Dejong K., Fogel D.8., Schwefel H-P., A History of Evolutionary Computation, in:
Back T., Fogel D.B., Michalewicz Z. (eds) Handbook of Evolutionary
Computation, Oxford University Press. New York, and Institute of Physics
Publishing, Bristol, 1997, pp. A2.3: 1-12.
39. Del Pino A., Heck P., Klink C., Klug J., Neumann J.P., Reuschling N.,
Constructing University Timetables using an Extended Artificial Immune System,
Interactive Systems and Technologies, 2009.
40. Dorigo M., Gambardella L.M., Ant colony system: A cooperative leaming
approach to the travelling salesman problem, IEEE Transactions on Evolutionary
Computation, 1997, pp.53-66.
134
41. Eiben A.E., Smith J.E., Introduction to Evolutionary Computing, chapter2: What is
an evolutionary algorithm, Springer, 2003.
42. EI-Mihoub T. A, Hopgood A. A., Nolle L., Battersby A, Hybrid genetic
algorithms: A review, Engineering Letters, vol. 3, issue no. 2,2006, pp. 124-137.
43. Engelbrecht A, Computational Intelligence: An Introduction, John Wiley and Sons
2002.
44. Farmer J.D., Packard N.H., Pere1son AS., The immune system, adaptation and
machine learning, Physica D, issue 22, 1986, pp. 187-204.
45. Fogel D.B, What is Evolutionary Computation?, IEEE Spectrum, Vol. 37, issue 2,
2000,pp.26,28-32.
46. Fogel L.J., Autonomous automata, Industrial Research, Vol. 4, 1962, pp. 14-9.
47. Forrest S., Perelson A.S., Allen L., Cherukuri R., Self-non-self discrimination in a
computer, Proceedings of the IEEE Symposium on Security and Privacy, IEEE
Computer Society, 1994, pp.202-212.
48. Futuyma D.J., Evolutionary Biology, Sinauer Associates, 1986.
49. Glover F., Tabu Search: A tutorial, Special Issue on the Practice of Mathematical
Programming, Interfaces, vol. 20, issue 1, 1990, pp.74-94.
50. Goldberg D., Deb K., A comparative analysis of selection schemes used in genetic
algorithms, Foundations of Genetic Algorithms, Morgan Kaufmann, 1991, pp. 69-
93.
51. Goldberg D.E., Lingle R.L., Alleles, loci, and the traveling salesman problem,
Proceedings of 1st Int. Conf. on Genetic Algorithms, Pittsburgh, PA, 1985, pp 154-
159.
135
52. Hart E., Timmis J., Application areas of AIS: the past, the present and the future,
Proceedings of the 4th International Conference on Artificial Immune Systems
(ICARIS), Lecture Notes in Computer Science, vol. 3627, 2005, pp 483-497,
Springer.
53. He Y., Hui S. C., Ming-Kit Lai E., Automatic Timetabling Using Artificial Immune
System, AAIM 2005, LNCS, vol. 3521,2005, pp. 55-65.
54. Holland J.H., Adaptation in Natural and Artificial Systems, Ann Arbor, MI:
University of Michigan Press, 1975.
55. International Timetabling Competition (lTC), available from:
http://www.idsia.chIFiles/ttcomp2002/IC Problem/node7.html, accessed February
2010.
56. Jeme N.K., Towards a network theory of the immune System, Annuals of
Immunology, vo1.l25, 1974, pp.373-389.
57. Junginger W., Timetabling in Germany: a survey, Interfaces, vol. 16, 1986, pp. 66-
74.
58. Kelsey J., Timmis J., x Immune Inspired Somatic Contiguous Hypermutation for
Function Optimization, in Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO 2003), Part I, 1986, pp. 207-218.
59. Kirkpatrick S., Gerlatt C. D. Jr., Vecchi M.P., Optimization by Simulated
Annealing, Science, vo1.220, 1983, pp. 671-680.
60. Kolodner J., Case-Based Reasoning, Morgan-Kaufmann, 1993.
61. Kumar V., Algorithms for constraint satisfaction problems: A survey, AI Magazine,
1992.
136
62. Lewis R., A survey of metaheuristic-based techniques for university timetabling
problems, OR Spectrum, vo1.30, issue 1,2008, pp. 167-190.
63. Lewis R.M.R., Metaheuristics for University Course Timetabling, Ph.D Thesis,
2006, pp.120-127.
64. Lii Z., Hao J.K, Adaptive Tabu Search for Course Timetabling, European Journal
of Operational Research Volume 200, Issue 1,2010, pp. 235-244.
65. Malim M.R., Khader A.T., A. Mustafa, Artificial Immune Algorithms for
University Timetabling, Proceedings of the 6th International Conference on the
Practice and Theory of Automated Timetabling, 2006 (a), pp.234-245.
66. Malim M.R., Khader A.T., Mustafa A., An immune-based approach to university
course timetabling: Negative selection algorithm, Proceedings of the 2nd IMT-GT
Regions Conference on Mathematics, Statistics and Applications, University of
Sains Malysia, Penang, 2006 (b), pp. 13-15.
67. Matzinger P., The danger model: a renewed sense of self, Science, vol.296, 2002,
pp.301-305.
68. Metaheuristics Network, available from: \vww.metaheuristics.net, accessed:
January 2010.
69. Muhlenbein H., Schlierkamp-Voosen D., Predictive models for the breeder genetic
algorithm. Evolutionary Computation, Vol. 1, Issue 1,1993.
70. Murray K., Muller T., Rudova H, Modeling and Solution of a Complex University
Course Timetabling Problem, Practice and Theory of Automated Timetabling,
Selected Revised Papers, Springer-Verlag LNCS vol. 3867,2007, pp. 189-209.
137
71. Nandhini M., Kanmani S., A Survey of Simulated Annealing Methodology for
University Course Timetabling, International Journal of Recent Trends in
Engineering, vol. I, issue 2, 2009, pp. 255-257.
72. Paechter B., Centre for Emergent Computing, School of Computing, Edinburgh
Napier University, available from: http://www.dcs.napier.ac.ukl-benp. accessed
May 2010.
73. Papadimitriou c.H., Steiglitz K., Combinatorial Optimization: Algorithms and
Complexity, Prentice-Hall, 1982, pp.3.
74. Parisi G., A Simple Model for the Immune Network, Proc. National Academy of
Science, USA, vol.87, issue 1, 1990, pp.429-433.
75. Qu R., He F., Burke E.K., Hybridizing Integer Programming Models within an
Adaptive Decomposition Approach for Exam Timetabling Problems, Proceedings
of the 4th Multidisciplinary International Scheduling Conference: Theory and
Applications (MISTA), Dublin, 2009, pp 435-446.
76. Rahman S.A., Bargiela A., Burke E. K., McCollum B., Ozcan E., Construction of
Examination Timetables Based on Ordering Heuristics, Proceedings of the 24th
International Symposium on Computer and Information Sciences, 2009.
77. Rapid Tables.com, available from:
http;//www.rapidtables.comimath/number/econstant.htm#exp. accessed September
2010.
78. Rechenberg 1., Cybernetic solution path of an experimental problem, Library
Translation No. 1122, Royal Aircraft Establishment, Farnborough, UK, 1965.
79. Rossi-Doria 0., Blum c., Knowles J., Sampels M., Socha K., Paechter B., A local
search for the timetabling problem, In Proceedings of the 4th International
Conference on Practice and Theory of Automated Timetabling (PATAT), 2002.
138
80. Rossi-Doria 0., Paechter B., A memetic algorithm for university course timetabling
in Proceedings of Combinatorial Optimization, 2004, pp.56.
81. Rossi-Doria 0., Sampels M., Chiarandini M., Knowles J., Manfrin M., Mastrolilli
M., Paquete L., Paechter B., A comparison of the performance of different
metaheuristics on the timetabling problem. Proceedings of PAT A T 2002, Burke
E.K., De Causmaecker P., (eds.), Springer Lecture Notes in Computer Science,
vo1.2740, 2003, pp. 329-351.
82. Schaerf A., A survey of automated timetabling, Artificial Intelligence Review, vol.
l3, issue 2, 1999, pp. 87-127.
83. Schaerf datasets, available from:
http://www.diegm.uniud.itlschaerf/projects/coursett.b , accessed February 2011.
84. Schwefel H-P., Kybernetische Evolutionals Strategie der exprimentellen Forschung
in der Stromungstechnik, Master's thesis, Technical University of Berlin, 1965.
85. Socha K., Sampels M., Manfrin M., Ant algorithms for the university course
timetabling problem with regard to the state-of-the-art, in Applications of
evolutionary computing, proceedings of EvoWorkshops, LNCS, vo1.2611, Berlin:
Springer, 2003, pp.334-45.
86. Soolmaz M., Esteki A., A Hybrid Genetic Algorithm for Curriculum Based Course
Timetabling Problem, in Proceedings of the 7th International Conference on the
Practice and Theory of Automated Timetabling (P A TA T08), Montreal, Canada,
2008.
87. Stutzle T., Hoos H.H., MAX-MIN Ant System. Future Generation Computer
Systems, vo1.16, 2000, pp.889-914.
139
88. Supplementary Infonnation on "A comparison of the perfonnance of different
metaheuristics on the timetabling problem", available from:
http://iridia. ulb.ac.be/supplIridiaSupp2002-00 l/index.html, accessed February
2010.
89. Taylor D.W., Come D.W., An investigation of negative selection for fault detection
in refrigeration systems. Proceedings of ICARIS, LNCS, Vol. 2787, pp. 34--45,
Springer, 2003.
90. Timmis A. J., Knight T., De Castro L.N, Hart E., An overview of artificial immune
systems in "Computation in Cells and Tissues: Perspectives and Tools for
Thought", Natural Computation Series, Springer, 2004, pp. 51-86.
91. Timmis J., Artificial immune systems - today and tomorrow. Natural Computing,
vol. 6, issue I, 2007, pp. 1-18.
92. Timmis, 1., Neal M., A resource limited artificial immune system for data analysis,
Knowledge Based Systems, vol. 14,2001, pp.121-130.
93. Tripathy A., School timetabling- A Case in Large Binary Integer Linear
Programming, Management Science, vo1.30, 1984, pp. 1473-1489.
94. Van den Broek J., Hurkens C., Woeginger G., Timetabling Problems at the TU
Eindhoven, Proceedings of the 6th International conference on the Practice and
Theory of Automated Timetabling PATAT, 2006, pp. 123-138
95. Watkins A., Boggess L., A resource limited artificial immune classifier, Congress
on Evolutionary Computation. Part of the World Congress on Computational
Intelligence, Honolulu, 2002, pp. 926--931.
96. Watkins A., Timmis J., Artificial Immune Recognition System (AIRS): Revisions
and Refinements, Proceedings of the 1st International Conference on Artificial
Immune Systems (ICARIS), University of Kent at Canterbury, 2002, pp. 173-181.
140
97. Weare R. F., Automated Examination Timetabling, PhD thesis, School of
Computer Science and Information Technology, University of Nottingham, 1995.
98. Welsh D.J.A., Powell M.B., An Upper Bound for the Chromatic Number of a
Graph and Its Application to Timetabling Problems, Compo Iml., vol. 10, 1967, pp.
85-86.
99. Whitley D., Starkweather T., Fuquay D., Scheduling problems and traveling
salesmen: the genetic edge recombination operator, Proceedings of 3rd Int. Conf.
on Genetic Algorithms, Fairfax, VA, Morgan Kaufmann, 1989, pp. 116-21.
100. Winston W.L, Operations research: Applications and Algorithms, 2nd Edn,
Kent Publishing Company, Boston, 1991.
101. Wren A., Scheduling, Timetabling and Rostering- A Special Relationship,
Practice and Theory of Automated Timetabling, Burke E. and Ross P. (eds.)
Springer-Verlag Lecture Notes in Computer Science vol. 1153, 1996, pp.44-75.
102. Wright A.H., Genetic algorithms for real parameter optimization,
Foundations of Genetic Algorithms, Morgan Kaufmann, 1991, pp. 205-218.
103. Yan H., Yu S.-N., A Multiple-Neighborhoods-Based Simulated Annealing
Algorithm for Timetable Problem, Lecture Notes in Computer Science, Springer
Verlag, vol. 3033, 2004, pp. 474-481.
104. Yeniay 0., Penalty function methods for constrained optimization with
genetic algorithms, Mathematical and Computational Applications, Vol. 10, issue
1,2005, pp.45-56.
141
wA..P .J ~';l E.~"" '+l-P ..).JJ.:! c.?Jl ~ wAfo ~.J ~';l (p ~Ji... :J.9'i1 '":-1431
.~';I~~
~ 1_ .• ,. 'I~l ~~'il ~u..J1 o\.A~.i'il ~~ ~.n '.c. J ~ '~I ~WI v- 'F" ...- ..J ~ . - =.J ~ ~~ .J _ . • •
• ~.J~I
4J ~I ~ 1_' ~~'il ~u..J1 ~"'iI ......... 1..i.A -;: .• t.-:I '~·'I,(··tl'·~· WI ~WI .J . u-- ~ . ~ ,... ~ ~J-- ~ f"J.:J ~.UM-" ••
. ~IJ'i' ,a.:.:ii u-l! ..sj~'i4 ~\a..~
:~.;l'~ NP-complete ty ,yo ~\...ujl ~ ~ (UCTP) wk....~1 wl..JjW ~jll J#I ~.., ~ I>~ .~I .clG ~ ~I I>~ ~~ J..,h.., w~ ~ r:i~.., w~1 ~I..,i :i:w ~ ,-,.,;.,':'
(UCTP) wh...~1 wl..JjW ~jll J..,~I ~.., ~ ~t..-=....., ~ 4......,Ip' i~ 4..lL....YI .'+h ~A",)':'I ,JI ~I w~ rWI ~1yU.....':iI..,
~ c:::~~1 ,-".'';i.., 'wl~1 ,yo ~..,lll... w~..J.) I.:il~ ~ ~ w4jli:.."nl ..J'-,U:;.I ~ .iil .(UCTP) wk....~1 wl..JjW ~jll J#I~.., ~ ~ ~ I.A.;'~J ~ ~I wL..:y..JL:.."n1
~~)UI~ L:wSyria - LataK/S branch
POBox 869 Lataklo Tel (+96341) 210045 Fax( +96341) 453977
Arab Academy for Science, Technology & Maritime Transport
~u.....ll • ,< ... -:11 ~ I, • ..J~ . J~
"~~I,)wl~I~..J~lJsJ..Ja.l~u....II~~ "
"AN EVOLUTIONARY-IMMUNE APPROACH FOR UNIVERSITY COURSE TIMETABLING"
(
: ~I.....;ll J: U,j!~1 oj:jI....~1 .lJJb 1.....;".) .l.A:o.1 . .lJ :~~,
(
(
.J~'~t",:,~~ Ganoub AI Wadi branch
Aswon-Sadot Road- POBox 11 Aswan Tel (+2097) 2332845/ 2332843
Fax:( +2097) 2332842
/
...... ,~.,.-A""' Cairo· Dokky branch
23 Doctor Sobky 5t Tel' (+202) 37481593/33365491
Fax:( +202) 33365492
www.aastmt.org
/
:~I oj:jl....'lll
~..l."........~L....~ . ..lJ:~~,
;;~~'~~-i"""""~1 Cairo· Misr EI Gedida branch
P.O. Box 2033 - Elhorria
""':O"""~,.~..I...A.. .. , Alexandria· Main Campus
P.o.Box 1029 - Miami EI Mashir Ismail St.-behind Sheraton Bldg.
Tel: (+202) 22685616 /22685615 Fax:(+202) 22685892
Miami Tel: (+203) 5565429 / 5481163 Fax:( +203) 5487786/5506042
Abukir Tel: (+203) 5622366/5622388 Fax:(+203) 5610950
\..; ~.-.,. ~'tI!'" 4,IAf~
"HI Ji,ill", ~.,l~I", ~.;ltJ.l ~.;aJ1 ~JlS'Jl ":'L..~I ~,Jl"Ai-, ..:.~bll ~
":'L..~I ~ -' ~~I ~~..)
~,Jl~1 .J oJIJ~1 ~
":'L..~I~~
~\...JI
~ ":'L..~I~
~MI JillI.J '-A-,Jl~I.J ~.Jla.ll4.tyUl 4o:J1.S'i1
2011
........ Wl~y.,~
":'L.."Ja.o.J ":'~I.:. ~
o~WI~4-
DIS 006.3 AW-EV
77130 C2
AN EVOLUTIONARY-IMMUNE APPROACH FOR UNIVERSITY COURSE TIMETABLING
77130