Knowledge Learning for Evolutionary Computation

Abstract

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