Soft Computing Applications

Pavel KrömerIT4 Knowledge Management

Outline

Soft computing

Research topics and applications

Soft computing

Roughly: search for approximate solutions to complex problemsGoals: practical solutions to real world problems, analysis of large data sets, classification, predictionMeans: bio-inspired algorithms, fuzzy systems, artificial neural networks, algebraic methods (matrix factorization), Emphasis of HPC aspects: parallel algorithms, many-core implementations, large data

IT4KM portfolio in SC

Evolutionary computation: genetic algorithms, genetic programming, artificial immune systems, differential evolutionSwarm intelligence: particle swarm optimization, ant colony optimizationArtificial neural networks: self organizing maps, PCA performing ANNs, maximum likelihood Hebbian learning, flexible neural treesHybrid methods: ANFIS

Research topics and applications

Design and implementation of EAs on the GPU

Manycore (many-threaded) SIMD versions of • DE, GA, GP

Design and implementation of EAs on the GPU

Combinatorial optimization

Data classification

Evolutionary clustering

P. Gajdos, P. Moravec: Intruder Data Classification Using GM-SOM, in CISIM (A. Cortesi, N. Chaki, K. Saeed, and S. T. Wierzchon, eds.), vol. 7564 of LNCS, pp 92-100, Springer 2012

P. Krömer, J. Platos, and V. Snásel, “Evolutionary clustering on CUDA,” in ECAI 2012 (L. D. Raedt, C. Bessière, D. Dubois, P. Doherty, P. Frasconi, F. Heintz, and P. J. F. Lucas, eds.), vol. 242 of Frontiers in Artificial Intelligence and Applications, pp. 909–910, IOS Press, 2012.

GPU Accelerated Genetic Clustering, SEAL 2012 (LNCS), Accepted.

Many-threaded Differential Evolution on the GPU (book chapter), Evolutionary Computation on Graphics Processing Units, Springer, the Natural Computing Series. Accepted.

+ submitted/invited papers to journal special issue (Concurrency and Computation: Practice and Experience, Wiley)

Information security, compression, (bio-)signal analysis

Intrusion detection, cryptography, data compression, EEG signal analysis

E. Ochodková, J. Dvorský, P. Krömer, and P. Tuček, “On fitness function based upon quasigroups power sequences,” in International Joint Conference CISIS12-ICEUTE12-SOCO12 Special Sessions, vol. 189 of Advances in Intelligent Systems and Computing, pp. 141–150, Springer Berlin Heidelberg, 2013. 10.1007/978-3-642-33018-6_14.E. 2012.J. Platos and P. Kromer, “Improving evolved alphabet using tabu set,” in Hybrid Artificial Intelligent Systems (E. Corchado, V. Snášel, A. Abraham, M. Wozniak, M. Graňa, and S.-B. Cho, eds.), vol. 7208 of Lecture Notes in Computer Science, pp. 655–666, Springer Berlin / Heidelberg, 2012. 10.1007/978-3-642-28942-2_59. 2012.P. Dohnálek, P. Gajdoš, T. Peterek and M. Penhaker, “Pattern Recognition in EEG Cognitive Signals Accelerated by GPU,” in International Joint Conference CISIS12-ICEUTE12-SOCO12 Special Sessions, vol. 189 of Advances in Intelligent Systems and Computing, pp. 477-485, Springer Berlin Heidelberg, 2013. 10.1007/978-3-642-33018-6_14.E. 2012.

Bio-inspired methods for combinatorial optimization

Focus on emerging / less used algorithms• Motivation the „No free

lunch“ theorem

(GAs), DE, AIS• New methods,

encodings

Problems• Linear Ordering Problem• Independent Task Scheduling

P. Kromer, V. Snasel, J. Platos, A. Abraham, and H. Ezakian, “Evolving schedules of independent tasks by differential evolution,” in INCoSA, vol. 329

of Studies in Computational Intelligence, pp. 79–94, Springer Berlin / Heidelberg, 2011.

Kromer, Platos, Snasel, „Independent Task Scheduling by Artificial Immune Systems“, Differential Evolution, and Genetic Algorithms, INCoS 2012, IEEE, pp 28-32. 2012.

Practical Results of Artificial Immune Systems for Combinatorial Optimization Problems, NaBIC 2012 (IEEE), Accepted.

+ submitted/invited papers to journal special issue (Cluster Computing: The Journal of Networks, Software Tools and Applications, Springer)

ANNs on the GPUs

Manycore (many-threaded) SIMD versions of• SOM, Neural PCA, MLHL

P. Gajdoš and J. Platoš: GPU Based Parallelism for Self-Organizing Map, IHCI 2011, Advances in Intelligent Systems and Computing, Springer 2013, Volume 179, Part 4, 231-242P. Krömer, E. Corchado, V. Snášel, J. Platoš, and L. García-Hernández, “Neural PCA and maximum likelihood hebbian learning on the GPU,” in Artificial Neural Networks and Machine Learning – ICANN 2012 (A. E. Villa, W. Duch, P. Érdi, F. Masulli, and G. Palm, eds.), vol. 7553 of Lecture Notes in Computer Science, pp. 132–139, Springer, 2012.

+ submitted/invited papers to journal special issues (Neurocomputing)

Evolution of fuzzy predictors and classifiers for data mining

Application of fuzzy IR principles and GP in data mining• An evolution of query

optimization algorithms• Used for classification,

function approximation,time series analysis

Steel products quality estimation

Photovoltaic power plant output prediction

Reactor tension cycles estimation

Traffic accident severity estimation

Intrusion detection

P. Krömer, J. Platoš, V. Snášel, and A. Abraham, “Fuzzy classification by evolutionary algorithms,” in IEEE SMC 2011, pp. 313 – 318, 2011.T. Beshah, D. Ejigu, A. Abraham, V. Snášel, and P. Krömer, “Knowledge discovery from road traffic accident data in ethiopia: Data quality, ensembling and trend analysis for improving road safety,” Neural Network World, vol. 22, no. 3, pp. 215–244, 2012.P. Krömer, T. Novosad, V. Snásel, V. Vera, B. Hernando, L. Garca-Hernandez, Hé. Quintian-Pardo, E. Corchado, R. Redondo, J. Sedano and A. E. Garcia, " Prediction of Dental Milling Time-Error by Flexible Neural Trees and Fuzzy Rules " in H. Yin, J. A. F. Costa and G. D. A. Barreto (Eds.), IDEAL 2012, LNCS, vol. 7435, pp. 842-849, Springer, 2012P. Kromer, L. Prokop, V. Snasel, S. Misak, J. Platos, and A. Abraham, “Evolutionary prediction of photovoltaic power plant energy production,” in GreenGEC@GECCO 2012, GECCO Companion ’12, (New York, NY, USA), pp. 35–42, ACM, 2012.

+ submitted/invited papers to journal special issues (Logic Journal of the IGPL)

Evolution of fuzzy predictors and classifiers for data mining

(Social) network analysisNetwork analysisForcoa.net DBLP analysisCommunity detectionTraffic routing

V. Snásel, P. Krömer, J. Platos, M. Kudelka, and Z. Horak, “On spectral partitioning of co-authorship networks,” in CISIM (A. Cortesi, N. Chaki, K. Saeed, and S. T. Wierzchon, eds.), vol. 7564 of LNCS, pp. 302–313, Springer, 2012.P. Krömer, V. Snásel, J. Platos, M. Kudelka, and Z. Horak, “An aco inspired weighting approach for the spectral partitioning of co-authorship networks,” in IEEE Congress on Evolutionary Computation, pp. 1–7, IEEE, 2012.M. Kudelka, Z. Horák, V. Snášel, P. Krömer, J. Platoš, and A. Abraham, “Social and swarm aspects of co-authorship network,” Logic Journal of the IGPL, vol. Special Issue: HAIS 2010, 2012.V. Snásel, P. Krömer, J. Platos, M. Kudelka, Z. Horak, and K. Wegrzyn-Wolska, “Two new methods for network analysis: Ant colony optimization and reduction by forgetting,” in Advances in Intelligent Web Mastering - 3, vol. 86 of Advances in Soft Computing, pp. 225–234, Springer, 2011.P. Krömer, J. Martinovic, M. Radecký, R. Tomis, and V. Snášel, “Ant colony inspired algorithm for adaptive traffic routing,” in Nature & Biologically Inspired Computing, Third World Congress on, NABIC 2011, pp. 336 – 341, IEEE, 2011.

Future work

GPU -> multi GPU -> (hybrid) GPU clustersSuperparallel meta-heuristicsHybrid bio-inspired algorithmsApplications