Abstract
Fitness sharing genetic algorithm is one of the most common used methods to deal with multimodal optimization problems. The algorithm requires peaks radii as the predefined parameter. It is very difficult to guess peaks radii for designers and decision makers in the real world applications. A novel self-adaptive annealing peaks radii control method has been suggested in this paper to deal with the problem. Peaks radii are coded into chromosomes and evolved while fitness sharing genetic algorithm optimizes the problem. The empirical results tested on the benchmark problems show that fitness sharing genetic algorithm with self-adaptive annealing peaks radii control method can find and maintain nearly all peaks steadily. This method is especially suitable for the problems whose peaks radii are difficult to estimate beforehand.
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Yu, X. (2005). Fitness Sharing Genetic Algorithm with Self-adaptive Annealing Peaks Radii Control Method. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539117_145
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DOI: https://doi.org/10.1007/11539117_145
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