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• W3040135268 abstract "Recent progress in the runtime analysis of evolutionary algorithms (EAs) has allowed the derivation of upper bounds on the expected runtime of standard steady-state GAs. These upper bounds have shown speed-ups of the GAs using crossover and mutation over the same algorithms that only use mutation operators (i.e., steady-state EAs) both for standard unimodal (i.e., OneMax) and multimodal (i.e., Jump) benchmark functions. These upper bounds suggest that populations are beneficial to the GA as well as higher mutation rates than the default 1/n rate. However, making rigorous claims was not possible because matching lower bounds were not available. Proving lower bounds on crossover-based EAs is a notoriously difficult task as it is hard to capture the progress that a diverse population can make. We use a potential function approach to prove a tight lower bound on the expected runtime of the (2 + 1) GA for OneMax for all mutation rates c/n with c" @default.
• W3040135268 created "2020-07-10" @default.
• W3040135268 creator A5018789989 @default.
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• W3040135268 date "2020-06-25" @default.
• W3040135268 modified "2023-09-23" @default.
• W3040135268 title "A tight lower bound on the expected runtime of standard steady state genetic algorithms" @default.
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• W3040135268 doi "https://doi.org/10.1145/3377930.3390212" @default.
• W3040135268 hasPublicationYear "2020" @default.
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