Adaptive elitist-population based genetic algorithm for multimodal function optimization

Authors:
Kwong-Sak Leung;Yong Liang
Affiliations:
Department of Computer Science & Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong;Department of Computer Science & Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong
Venue:
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Year:
2003

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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.