ICCS 2007: Computational Science – ICCS 2007 pp 920-927 | Cite as
Sexual Selection Mechanism for Agent-Based Evolutionary Computation
Conference paper
Abstract
Sexual selection mechanism can be used in evolutionary algorithms in order to introduce and maintain useful population diversity. In this paper the sexual selection mechanism for agent-based evolutionary algorithms is presented. Proposed co-evolutionary multi-agent system with sexual selection is applied to multi-modal optimization problems and compared to “classical” evolutionary algorithms.
Keywords
agent-based evolutionary computation sexual selection mechanism Download
to read the full conference paper text
References
- 1.Bäck, T., Fogel, D., Michalewicz, Z.: Handbook of Evolutionary Computation. Oxford University Press, Oxford (1997)MATHGoogle Scholar
- 2.Dreżewski, R.: A model of co-evolution in multi-agent system. In: Mařík, V., Müller, J.P., Pěchouček, M. (eds.) CEEMAS 2003. LNCS (LNAI), vol. 2691, pp. 314–323. Springer, Heidelberg (2003)CrossRefGoogle Scholar
- 3.Dreżewski, R.: Co-evolutionary multi-agent system with speciation and resource sharing mechanisms. Computing and Informatics 25(4), 305–331 (2006)MATHGoogle Scholar
- 4.Dreżewski, R., Siwik, L.: Multi-objective optimization using co-evolutionary multi-agent system with host-parasite mechanism. In: Alexandrov, V.N., van Albada, G.D., Sloot, P.M.A., Dongarra, J.J. (eds.) ICCS 2006. LNCS, vol. 3993, pp. 871–878. Springer, Heidelberg (2006)CrossRefGoogle Scholar
- 5.Gavrilets, S.: Models of speciation: what have we learned in 40 years? Evolution 57(10), 2197–2215 (2003)Google Scholar
- 6.Krebs, J.R., Davies, N.B.: An Introduction to Behavioural Ecology. Blackwell Science Ltd., Malden (1993)Google Scholar
- 7.Mahfoud, S.W.: Niching methods for genetic algorithms. PhD thesis, University of Illinois at Urbana-Champaign, Urbana, IL, USA (1995)Google Scholar
- 8.Ratford, M., Tuson, A.L., Thompson, H.: An investigation of sexual selection as a mechanism for obtaining multiple distinct solutions. Technical Report 879, Department of Artificial Intelligence, University of Edinburgh (1997)Google Scholar
- 9.Sánchez-Velazco, J., Bullinaria, J.A.: Gendered selection strategies in genetic algorithms for optimization. In: Rossiter, J.M., Martin, T.P. (eds.) Proceedings of the UK Workshop on Computational Intelligence (UKCI 2003), Bristol, UK, pp. 217–223. University of Bristol (2003)Google Scholar
- 10.Todd, P.M., Miller, G.F.: Biodiversity through sexual selection. In: Langton, C.G., Shimohara, T. (eds.) Artificial Life V: Proceedings of the Fifth International Workshop on the Synthesis and Simulation of Living Systems (Complex Adaptive Systems), pp. 289–299. MIT Press, Cambridge (1997)Google Scholar
- 11.Ursem, R.K.: Multinational evolutionary algorithms. In: Angeline, P.J., Michalewicz, Z., Schoenauer, M., Yao, X., Zalzala, A. (eds.) Proceedings of the 1999 Congress on Evolutionary Computation (CEC-1999), pp. 1633–1640. IEEE Computer Society Press, Piscataway (1999)CrossRefGoogle Scholar
- 12.Watson, J.-P.: A performance assessment of modern niching methods for parameter optimization problems. In: Banzhaf, W., Daida, J., Eiben, A.E., Garzon, M.H., Honavar, V., Jakiela, M., Smith, R.E. (eds.) GECCO-99: Proceedings of the Genetic and Evolutionary Computation Conference, vol. 1, pp. 702–709. Morgan Kaufmann, San Francisco (1999)Google Scholar
Copyright information
© Springer Berlin Heidelberg 2007