Abstract
Genetic algorithms with sharing are well known for tackling multimodal function optimization problems. In this paper, a sharing scheme using a clustering methodology is introduced and compared with the classical sharing scheme. It is shown from the simulation on test functions and on a practical problem that the proposed scheme proceeds faster than the classical scheme with a performance remaining as good as the classical one. In addition, the proposed scheme reveals unknown multimodal function structure when a priori knowledge about the function is poor. Finally, introduction of a mating restriction inside the proposed scheme is investigated and shown to increase the optimization quality without requiring additional computation efforts.
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© 1993 Springer-Verlag/Wien
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Yin, X., Germay, N. (1993). A Fast Genetic Algorithm with Sharing Scheme Using Cluster Analysis Methods in Multimodal Function Optimization. In: Albrecht, R.F., Reeves, C.R., Steele, N.C. (eds) Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-7533-0_65
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DOI: https://doi.org/10.1007/978-3-7091-7533-0_65
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-82459-7
Online ISBN: 978-3-7091-7533-0
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