On the choice of the mutation probability for the (1+1) EA

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 superpolynornial 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.