Optimal static mutation strength distributions for the (1 + λ ) evolutionary algorithm on OneMax
Maxim Buzdalov, Carola Doerr · Proceedings of the Genetic and Evolutionary Computation Conference · 2021
Most evolutionary algorithms have parameters, which allow a great flexibility in controlling their behavior and adapting them to new problems. To achieve the best performance, it is often needed to control some of the parameters during optimization, which gave rise to various parameter control methods. In recent works, however, similar advantages have been shown, and even proven, for sampling parameter values from certain, often heavy-tailed, fixed distributions. This produced a family of algorithms currently known as "fast evolution strategies" and "fast genetic algorithms".