Abstract
When evolutionary algorithms are used for function optimization, they perform a heuristic search that is influenced by many parameters. Here, the choice of the mutation probability is investigated. It is shown for a non-trivial example function that the most recommended choice for the mutation probability 1/n is by far not optimal, i. e., it leads to a superpolynomial running time while another choice of the mutation probability leads to a search algorithm with expected polynomial running time. Furthermore, a simple evolutionary algorithm with an extremely simple dynamic mutation probability scheme is suggested to overcome the difficulty of finding a proper setting for the mutation probability.
This work was supported by the Deutsche Forschungsgemeinschaft (DFG) as part of the Collaborative Research Center “computational Intelligence” (531).
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Jansen, T., Wegener, I. (2000). On the Choice of the Mutation Probability for the (1+1) EA. In: Schoenauer, M., et al. Parallel Problem Solving from Nature PPSN VI. PPSN 2000. Lecture Notes in Computer Science, vol 1917. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45356-3_9
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DOI: https://doi.org/10.1007/3-540-45356-3_9
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