Change detection in dynamic fitness landscapes with time-dependent constraints

Evolutionary algorithms operating in a dynamic environment (fitness landscape and constraints) need information about the moment of change to achieve proper optimization result. For solving the change point detection task we proposed a method based on population statistics. We show in numerical experiments that such an approach allows a successful identification of constraint and/or landscape changes.