Abstract
Ensemble methods are widely used to improve decision making in the field of statistics and machine learning. On average, the collective solution of multiple algorithms provides better performance than could be obtained from any of the constituent algorithms. The ensemble concept can be also used in the field of evolutionary algorithms. The main idea is to include many search algorithms in the ensemble and to design effective control of interaction of algorithms. Such interaction is implemented in different forms of island models, coevolutionary schemes, population-based algorithm portfolios and others. In this paper, a metaheuristic for designing multi-strategy genetic algorithm for multimodal optimization is proposed. Multimodal optimization is the problem of finding many or all global and local optima. In recent years many efficient multimodal techniques have been proposed in the field of population-based nature-inspired search algorithms. The majority of techniques are designed for real-valued problems. At the same time many real-world problems contain variables of many different types, including integer, rank, binary and others. In this case, a binary representation is used. There is a lack of efficient approaches for problems with binary representation. Moreover, binary and binarized problems are usually “black-box” optimization problems, thus there exists the problem of choosing a suitable algorithm and fine tuning it for a certain problem. The proposed approach contains many different multimodal genetic algorithms, which implement different search strategies. The metaheuristic adaptively controls the interactions of many search techniques and leads to the self-configuring solving of problems with a priori unknown structure. We present the results of numerical experiments for classical binary benchmark problems and benchmark problems from the CEC 2013 competition on multimodal optimization. We also present the results for some real-world problems.
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References
Bandaru, S., Deb, K.: A parameterless-niching-assisted bi-objective approach to multimodal optimization. In: Proceedings of 2013 IEEE Congress on Evolutionary Computation (CEC 2013), pp. 95–102 (2013)
Bessaou, M., Pétrowski, A., Siarry, P.: Island model cooperating with speciation for multimodal optimization. In: Schoenauer, M., Deb, K., Rudolph, G., Yao, X., Lutton, E., Merelo, J.J., Schwefel, H.-P. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 437–446. Springer, Heidelberg (2000). doi:10.1007/3-540-45356-3_43
Das, S., Maity, S., Qub, B.-Y., Suganthan, P.N.: Real-parameter evolutionary multimodal optimization: a survey of the state-of-the art. Swarm Evol. Comput. 1, 71–88 (2011)
Deb, K., Saha, A.: Finding multiple solutions for multimodal optimization problems using a multi-objective evolutionary approach. In: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, GECCO 2010, pp. 447–454. ACM, New York (2010)
Epitropakis, M.G., Li, X., Burke, E.K.: A dynamic archive niching differential evolution algorithm for multimodal optimization. In: Proceedings of 2013 IEEE Congress on Evolutionary Computation (CEC 2013), pp. 79–86 (2013)
Ishibuchi H.: Hybridization of fuzzy GBML approaches for pattern classification problems. IEEE Trans. Syst. Man Cybern. B Cybern. 35(2), 359–365 (2005)
KEEL (Knowledge Extraction based on Evolutionary Learning). http://www.keel.es
Li, X., Engelbrecht, A., Epitropakis, M.: 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 (2013)
Li, X., Engelbrecht, A., Epitropakis, M.G.: Benchmark functions for CEC 2013 special session and competition on niching methods for multimodal function optimization. Evolutionary Computation and Machine Learning Group, RMIT University, Melbourne, VIC, Australia. Technical report (2013)
Liu, Y., Ling, X., Shi, Z., Lv, M., Fang, J., Zhang, L.: A survey on particle swarm optimization algorithms for multimodal function optimization. J. Softw. 6(12), 2449–2455 (2011)
Molina, D., Puris, A., Bello, R., Herrera, F.: Variable mesh optimization for the 2013 CEC special session niching methods for multimodal optimization. In: Proceedings of 2013 IEEE Congress on Evolutionary Computation (CEC 2013), pp. 87–94 (2013)
Preuss, M., Stoean, C., Stoean, R.: Niching foundations: basin identification on fixed-property generated landscapes. In: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, GECCO 2011, pp. 837–844 (2011)
Preuss, M., Wessing, S.: Measuring multimodal optimization solution sets with a view to multiobjective techniques. In: Emmerich, M., et al. (eds.) EVOLVE – A Bridge Between Probability, Set Oriented Numerics, and Evolutionary Computation IV. AISC, vol. 227, pp. 123–137. Springer, Heidelberg (2013)
Preuss, M.: Tutorial on multimodal optimization. In: Proceedings of the 13th International Conference on Parallel Problem Solving from Nature, PPSN 2014, Ljubljana, Slovenia (2014)
Qu, B., Liang, J., Suganthan, P.N., Chen, T.: Ensemble of clearing differential evolution for multi-modal optimization. In: Tan, Y., Shi, Y., Ji, Z. (eds.) ICSI 2012. LNCS, vol. 7331, pp. 350–357. Springer, Heidelberg (2012). doi:10.1007/978-3-642-30976-2_42
Semenkin, E., Semenkina, M.: Self-configuring genetic algorithm with modified uniform crossover operator. In: Tan, Y., Shi, Y., Ji, Z. (eds.) ICSI 2012. LNCS, vol. 7331, pp. 414–421. Springer, Heidelberg (2012). doi:10.1007/978-3-642-30976-2_50
Singh, G., Deb, K.: Comparison of multi-modal optimization algorithms based on evolutionary algorithms. In: Proceedings of the Genetic and Evolutionary Computation Conference, Seattle, pp. 1305–1312 (2006)
Sopov E., Stanovov V., Semenkin E.: Multi-strategy multimodal genetic algorithm for designing fuzzy rule based classifiers. In: Proceedings of 2015 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2015), Cape Town, South Africa, pp. 167–173 (2015)
Sopov, E.: A self-configuring metaheuristic for control of multi-strategy evolutionary search. In: Tan, Y., Shi, Y., Buarque, F., Gelbukh, A., Das, S., Engelbrecht, A. (eds.) ICSI 2015. LNCS, vol. 9142, pp. 29–37. Springer, Heidelberg (2015). doi:10.1007/978-3-319-20469-7_4
UC Irvine Machine Learning Repository. http://archive.ics.uci.edu/ml/
Yu, E.L., Suganthan, P.N.: Ensemble of niching algorithms. Inf. Sci. 180(15), 2815–2833 (2010)
Acknowledgements
The research was supported by President of the Russian Federation grant (MK-3285.2015.9).
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Sopov, E. (2017). Self-configuring Ensemble of Multimodal Genetic Algorithms. In: Merelo, J.J., et al. Computational Intelligence. IJCCI 2015. Studies in Computational Intelligence, vol 669. Springer, Cham. https://doi.org/10.1007/978-3-319-48506-5_4
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