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Adaptive Elitist-Population Based Genetic Algorithm for Multimodal Function Optimization
Kwong-Sak Leung and Yong Liang
Department of Computer Science & Engineering,
The Chinese University of Hong Kong,
Shatin, N.T., Hong Kong
{ksleung,yliang}@cse.cuhk.edu.hk
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.
LNCS 2723, p. 1160 ff.
lncs@springer.de
© Springer-Verlag Berlin Heidelberg 2003