Abstract
This paper introduces a new technique called adaptive elitist-population search method for allowing unimodal function optimization methods to be extended to efficiently locate all optima of multimodal problems. The technique is based on the concept of adaptively adjusting the population size according to the individuals’ dissimilarity and the novel elitist genetic operators. Incorporation of the technique in any known evolutionary algorithm leads to a multimodal version of the algorithm. As a case study, genetic algorithms(GAs) have been endowed with the multimodal technique, yielding an adaptive elitist-population based genetic algorithm(AEGA). The AEGA has been shown to be very efficient and effective in finding multiple solutions of the benchmark multimodal optimization problems.
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References
D.E. Goldberg and J. Richardson, Genetic algorithms with sharing for multimodal function optimization, Proc. 2 nd ICGA, pp.41–49, 1987
Eshelman. L.J. The CHC adaptive search algorithm: how to have safe search when engaging in nontraditional genetic recombination. In Rawlins, G.J.E., editor, Foundation of Genetic Algorithms pp.265–283, Morgan Kaufmann, San Mateo, California, 1991
Holland J.H. and Adaptation in Natural and ArtificialSystem. University of Michigan Press, Ann Arbor, Michigan, 1975.
K.A. De Jong, An analysis of the behavior of a class of genetic adaptive systems, Doctoral dissertation, Univ. of Michigan, 1975
L.N. Castro and F.J. Zuben, Learning and Optimization Using the Clonal Selection Principle, IEEE Transactions on EC, vol. 6 pp.239–251, 2002
Sarma. J. and De Jong, Generation gap methods. Handbook of Evolutionary Computation, pp.C2.7:1–C2.7:5, 1997
S.W. Mahfoud. Niching Methods for Genetic algorithms, Doctoral Dissertation, IlliGAL Report 95001, University of Illinois at Urbana Champaign, Illinois Genetic Algorithm Laboratory, 1995
Whitley. D., The GENITOR algorithm and selection pressure: why rank-based allocation of reproductive trials is best. Proc. 3rd ICGA, pp.116–121, 1989
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Leung, KS., Liang, Y. (2003). Adaptive Elitist-Population Based Genetic Algorithm for Multimodal Function Optimization. In: Cantú-Paz, E., et al. Genetic and Evolutionary Computation — GECCO 2003. GECCO 2003. Lecture Notes in Computer Science, vol 2723. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45105-6_124
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DOI: https://doi.org/10.1007/3-540-45105-6_124
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