Ensemble of Multimodal Genetic Algorithms for Design and Decision Making Support Problems

Evgenii Sopov, Eugene Semenkin, Ilia Panfilov

2016

Abstract

Many problems of design and decision making support can be stated as optimization problems. For real-world problems, sometimes it is necessary to obtain many alternative solutions to the problem. In this case multimodal approach can be used. The goal of multimodal optimization (MMO) is to find all optima (global and local) or a representative subset of all optima. In recent years many efficient nature-inspired techniques have been proposed for real-valued MMO problems. At the same time, real-world design and decision making support problems may contain variables of many different types, including integer, rank, binary and others. In this case, the weakest representation (namely binary representation) is used. Unfortunately, there is a lack of efficient approaches for problems with binary representation. In this study, a novel approach based on a selective hyper-heuristic in a form of ensemble for designing multi-strategy genetic algorithm is proposed. The approach controls the interactions of many search techniques (different genetic algorithms for MMO) and leads to the self-configuring solving of problems with a priori unknown structure. The results of numerical experiments for benchmark problems from the CEC competition on MMO and for some real-world problems are presented and discussed.

References

  1. Bandaru, S., Deb, K. (2013). A parameterless-nichingassisted bi-objective approach to multimodal optimization. In Proc. 2013 IEEE Congress on Evolutionary Computation (CEC'13). pp. 95-102.
  2. Bessaou, M., Petrowski, A., Siarry, P. (2000). Island Model Cooperating with Speciation for Multimodal Optimization. Parallel Problem Solving from Nature PPSN VI, Lecture Notes in Computer Science, Volume 1917. pp. 437-446.
  3. Bianchi, L., Dorigo, M., Gambardella, L.M., Gutjahr, W.J. (2009). A survey on metaheuristics for stochastic combinatorial optimization. Natural Computing, Volume 8, issue 2. pp. 239-287.
  4. Boussaida, I., Lepagnotb, J., Siarryb, P. (2013). A survey on optimization metaheuristics. Information Sciences, Volume 237. pp. 82-117.
  5. Burke, E.K., Hyde, M., Kendall, G., Ochoa, G., Ozcan, E., Woodward, J.R. (2010). A Classification of Hyperheuristic Approaches. Handbook of Metaheuristics, Volume 146 of the series International Series in Operations Research & Management Science. pp. 449- 468.
  6. Das, S., Maity, S., Qub, B.-Y., Suganthan, P.N. (2011). Real-parameter evolutionary multimodal optimization: a survey of the state-of-the art. Swarm and Evolutionary Computation, 1. pp. 71-88.
  7. Deb, K., Saha, A. (2010). Finding Multiple Solutions for Multimodal Optimization Problems Using a MultiObjective Evolutionary Approach. In Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, GECCO 2010. ACM, New York. pp. 447-454.
  8. Epitropakis, M.G., Li, X., Burke, E.K. (2013). A dynamic archive niching differential evolution algorithm for multimodal optimization. In Proc. 2013 IEEE Congress on Evolutionary Computation (CEC'13). pp. 79-86.
  9. Goldberg, D. (1989). Genetic Algorithms in Search, Optimization and Machine Learning. Reading. MA: Addison-Wesley.
  10. Holland, J. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.
  11. ics.uci.edu (2015). UC Irvine Machine Learning Repository. [online] Available at: http://archive.ics.uci.edu/ml/
  12. Ishibuchi H. (2005). Hybridization of fuzzy GBML approaches for pattern classification problems. In IEEE Trans. on Systems, Man, and Cybernetics - Part B: Cybernetics, Volume 35, Issue 2. pp. 359-365.
  13. Kaklaukas, A. (2015). Biometric and Intelligent Decision Making Support. Intelligent Systems Reference Library, Vol. 81. 2015, XII. Springer-Verlag, Berlin.
  14. KEEL (2015). KEEL, Knowledge Extraction based on Evolutionary Learning. [online] Available at: http://www.keel.es.
  15. Li, B., Li. J., Tang, K., Yao, X. (2015). Many-Objective Evolutionary Algorithms: A Survey. ACM Computing Surveys (CSUR), v.48 n.1. pp. 1-35.
  16. Li, X., Engelbrecht, A., Epitropakis, M.G. (2013a). Benchmark functions for CEC'2013 special session and competition on niching methods for multimodal function optimization. Evol. Comput. Mach. Learn. Group, RMIT University, Melbourne, VIC, Australia. Tech. Rep.
  17. Li, X., Engelbrecht, A., Epitropakis, M. (2013b). Results of the 2013 IEEE CEC Competition on Niching Methods for Multimodal Optimization. Report presented at 2013 IEEE Congress on Evolutionary Computation Competition on: Niching Methods for Multimodal Optimization.
  18. Liu, Y., Ling, X., Shi, Zh., Lv, M., Fang. J., Zhang, L. (2011). A Survey on Particle Swarm Optimization Algorithms for Multimodal Function Optimization. Journal of Software, Vol. 6, No. 12. pp. 2449-2455.
  19. Maashi M., Kendall, G., Özcan, E. (2015). Choice function based hyper-heuristics for multi-objective optimization. Applied Soft Computing, Volume 28. pp. 312-326.
  20. Molina, D., Puris, A., Bello, R., Herrera, F. (2013). Variable mesh optimization for the 2013 CEC special session niching methods for multimodal optimization. In Proc. 2013 IEEE Congress on Evolutionary Computation (CEC'13). pp. 87-94.
  21. Pillay, N. (2015). An Overview of Evolutionary Algorithms and Hyper Heuristics. In 2015 IEEE Congress on Evolutionary Computation (IEEE CEC 2015), Sendai, Japan. [online] Available at: http://www.cs.usm.maine.edu/congdon/Conferences/ CEC2015/Pillay.CEC2015.tutorial.pdf.
  22. Preuss, M., Wessing, S. (2013). Measuring multimodal optimization solution sets with a view to multiobjective techniques. EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation IV. AISC, vol. 227, Springer, Heidelberg. pp. 123-137.
  23. Qu, B., Liang, J., Suganthan P.N., Chen, T. (2012). Ensemble of Clearing Differential Evolution for Multimodal Optimization. Advances in Swarm Intelligence Lecture Notes in Computer Science, Volume 7331. pp. 350-357.
  24. Ray, T., Liew K.M. (2002). A Swarm Metaphor for Multiobjective Design Optimization. Engineering Optimization, 34. pp. 141-153.
  25. Ross, P. (2005). Hyper-Heuristics. Search Methodologies. pp. 529-556.
  26. Sen, P., Yang, J.-B. (2012). Multiple Criteria Decision Support in Engineering Design. Springer Science & Business Media.
  27. Singh, G., Deb, K. (2006). Comparison of multi-modal optimization algorithms based on evolutionary algorithms. In Proceedings of the Genetic and Evolutionary Computation Conference, Seattle. pp. 1305-1312.
  28. Sopov, E., Stanovov, V., Semenkin, E. (2015). Multistrategy Multimodal Genetic Algorithm for De-signing Fuzzy Rule Based Classifiers. In Proceedings of 2015 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2015), Cape Town, South Africa. pp.167- 173.
  29. Sopov, E. (2015a). A Self-configuring Metaheuristic for Control of Multi-Strategy Evolutionary Search. ICSICCI 2015, Part III, LNCS 9142. pp. 29-37.
  30. Sopov, E. (2015b). Multi-strategy Genetic Algorithm for Multimodal Optimization. In Proceedings of the 7th International Joint Conference on Computational Intelligence (IJCCI 2015) - Volume 1: ECTA, Portugal. pp. 55-63.
  31. Yu, E.L., Suganthan, P.N. (2010). Ensemble of niching algorithms. Information Sciences, Vol. 180, No. 15. pp. 2815-2833.
Download


Paper Citation


in Harvard Style

Sopov E., Semenkin E. and Panfilov I. (2016). Ensemble of Multimodal Genetic Algorithms for Design and Decision Making Support Problems . In Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-198-4, pages 160-167. DOI: 10.5220/0005976401600167


in Bibtex Style

@conference{icinco16,
author={Evgenii Sopov and Eugene Semenkin and Ilia Panfilov},
title={Ensemble of Multimodal Genetic Algorithms for Design and Decision Making Support Problems},
booktitle={Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2016},
pages={160-167},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005976401600167},
isbn={978-989-758-198-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Ensemble of Multimodal Genetic Algorithms for Design and Decision Making Support Problems
SN - 978-989-758-198-4
AU - Sopov E.
AU - Semenkin E.
AU - Panfilov I.
PY - 2016
SP - 160
EP - 167
DO - 10.5220/0005976401600167