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Self-adjusting mutation rates with provably optimal success rules


The one-fifth success rule is one of the best-known and most widely accepted techniques to control the parameters of evolutionary algorithms. While it is often applied in the literal sense, a common interpretation sees the one-fifth success rule as a family of success-based updated rules that are determined by an update strength F and a success rate s. We analyze in this work how the performance of the (1+1) Evolutionary Algorithm (EA) on LeadingOnes depends on these two hyper-parameters. Our main result shows that the best performance is obtained for small update strengths F = 1+o(1) and success rate 1/e. We also prove that the runtime obtained by this parameter setting is asymptotically optimal among all dynamic choices of the mutation rate for the (1+1) EA, up to lower order error terms. We show similar results for the resampling variant of the (1+1) EA, which enforces to flip at least one bit per iteration.
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hal-02175768 , version 1 (14-01-2020)



Benjamin Doerr, Carola Doerr, Johannes Lengler. Self-adjusting mutation rates with provably optimal success rules. GECCO 2019 - The Genetic and Evolutionary Computation Conference, Jul 2019, Prague, Czech Republic. pp.1479-1487, ⟨10.1145/3321707.3321733⟩. ⟨hal-02175768⟩
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