Change detection in dynamic fitness landscapes: An immunological approach

Detecting the point in time where a dynamic fitness landscape changes is vital for a considerable number of diversity management schemes that used in evolutionary algorithms employed for solving dynamic optimization problems. Here, we introduce a change detector based on principles of artificial immune systems, namely a negative selection algorithm. We show in numerical experiments that such an immunological approach allows to solve the change detection problem successfully for dynamic fitness landscapes.