Directed mutation-a new self-adaptation for evolution strategies

Evolution strategies are a powerful variant of the evolutionary algorithms, which themselves are probabilistic optimization methods. Many sophisticated methods have been developed to increase the convergence of evolution strategies. Self-adaptation is one of these methods and allows an evolution strategy to adapt to the goal function. Nevertheless most real world applications of evolution strategies do not make use of the self-adaptation. The authors analyze the reasons for this and introduce a new type of self-adaptation that overcomes the disadvantages of the known types. Experimental results based on the sphere model are presented, which show an significant increase of performance.