Jansen, ThomasWegener, Ingo2004-12-072004-12-0720002001-10-17http://hdl.handle.net/2003/539510.17877/DE290R-7285When evolutionary algorithms are used for function optimization, they perform a heuristic search that is in fluenced 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.enUniversität DortmundReihe Computational Intelligence ; 92004report