How Genetic Algorithms Really Work I.mutation and Hillclimbing

In this paper mutation and hillclimbing are analyzed with the help of representative binary functions and a simple asexual evolutionary algorithm. An optimal mutation rate is computed and a good agreement t o n umerical results is shown. In most of the cases the optimal mutation rate is proportional to the length of the chromosome. For deceptive functions an evolutionary algorithm with a good hillclimbing strategy and a reasonable mutation rate performs best. The paper is a rst step towards a statistical analysis of genetic algorithms. It shows that the power of mutation has been underestimated in traditional genetic algorithms.

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