Operator adaptation in evolutionary computation and its application to structure optimization of neural networks

Christian Igel, Martin Kreutz

Abstract: The problem of finding a suitable neural network topology for a given task is often solved by evolutionary computation. In this paper, we show empirically that online adaptation of the search strategy can increase the performance of evolutionary structure optimization. After a brief overview of strategy adaptation in evolutionary computation, we present a general method for adjusting the probabilities of applying variation operators. In example problems the adaptation method leads to faster optimization and better solutions when used in structure optimization of neural networks. We observe that during the evolutionary process the operator probabilities change in an intuitive way depending on the task.