Within Genetic Algorithms (GAs) the mutation rate is mostly handled as a global, external parameter, which is constant over time or exogeneously changed over time.

In this paper a new approach is presented, which transfers a basic idea from Evolution Strategies (ESs) to GAs. Mutation rates are changed into endogeneous items which are adapting during the research process. First experimental results are presented, which indicate that enviroment-dependent self-adaption of appropiate settings for the mutation rate is possible even for GAs.

Furthermore, the reduction of the number of external parameters of a GA is seen as a first step towards achieving a problem-dependent self-adaptation of the algorithm.