Niching in Evolution Strategies and Its Application to Laser Pulse Shaping
Evolutionary Algorithms (EAs), popular search methods for optimization problems, are known for successful and fast location of single optimal solutions. However, many complex search problems require the location and maintenance of multiple solutions. Niching methods, the extension of EAs to address this issue, have been investigated up to date mainly within the field of Genetic Algorithms (GAs), and their applications were limited to low-dimensional search problems.
In this paper we present in detail the background for niching methods within Evolution Strategies (ES), and discuss two ES niching methods, which have been introduced recently and have been tested only for theoretical functions. We describe the application of those ES niching methods to a challenging real-life high-dimensional optimization problem, namely Femtosecond Laser Pulse Shaping. The methods are shown to be robust and to achieve satisfying results for the given problem.
KeywordsEvolution Strategy Search Point Random Genetic Drift Evolution Strategy Covariance Matrix Adaptation
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- 1.Mahfoud, S.: Niching Methods for Genetic Algorithms. PhD thesis, University of Illinois at Urbana Champaign (1995)Google Scholar
- 2.Shir, O.M., Bäck, T.: Niching in evolution strategies. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2005, ACM Press, New York (2005)Google Scholar
- 3.Shir, O.M., Bäck, T.: Dynamic niching in evolution strategies with covariance matrix adaptation. In: Proceedings of the 2005 Congress on Evolutionary Computation CEC 2005, IEEE Press, Piscataway (2005)Google Scholar
- 5.Deb, K., Goldberg, D.E.: An investigation of niche and species formation in genetic function optimization. In: Proceedings of the third international conference on Genetic algorithms, pp. 42–50. Morgan Kaufmann Publishers Inc, San Francisco (1989)Google Scholar
- 6.Bäck, T.: Selective pressure in evolutionary algorithms: A characterization of selection mechanisms. In: Michalewicz, Z., Schaffer, J.D., Schwefel, H.P., Fogel, D.B., Kitano, H. (eds.) Proc. First IEEE Conf. Evolutionary Computation (ICEC 1994) Orlando FL., vol. 1, pp. 57–62. IEEE Press, Piscataway (1994)Google Scholar
- 8.Schönemann, L., Emmerich, M., Preuss, M.: On the extiction of sub-populations on multimodal landscapes. In: Proc.of the Int’l Conf.on Bioinspired optimization Methods and their Applications, BIOMA, Jožef Stefan Institute, Slovenia (2004), pp. 31–40 (2004)Google Scholar
- 9.Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. MIT Press, Cambridge (1992)Google Scholar
- 10.Jong, K.A.D.: An analysis of the behavior of a class of genetic adaptive systems. PhD thesis (1975)Google Scholar
- 11.Miller, B.L., Shaw, M.J.: Genetic algorithms with dynamic niche sharing for multimodal function optimization. In: Proceedings of the 1996 IEEE International Conference on Evolutionary Computation (ICEC 1996), New York, NY, USA (1996)Google Scholar