Knowledge Learning for Evolutionary Computation

Evolutionary computation (EC) is a kind of meta-heuristic algorithm that takes inspiration from natural evolution and swarm intelligence behaviors. In the EC algorithm, there huge amount data generated during evolutionary process. These reflect behavior therefore mining utilizing these can obtain promising knowledge for improving effectiveness efficiency algorithms to better solve optimization problems. Considering this inspired by ability human beings acquire historical successful experiences their predecessors, paper proposes novel paradigm, named learning (KLEC). The KLEC aims learn library guide behaviors individuals based on library. includes two main processes “learning knowledge” “utilizing evolution”. First, maintains model updates collected in every generation. Second, not only adopts operation but also utilizes evolution. generic effective framework, we propose instances KLEC, which are learning-based differential particle optimization. Also, combine framework with several state-of-the-art algorithms, showing performance be significantly enhanced incorporating framework.