Evolving in a Changing World

There is increasing interest in using evolutionary algorithms to solve problems in which the fitness landscape is nonstationary. Not surprisingly our favorite EAS developed for static optimization problems don’t, fare too well in changing worlds. In this paper we, explore the issues involved, identify some key elements, and provide a more structured framework for designing EAs that perform well in dynamic environments.