Abstract
Evolutionary computation (EC) is one of the fastest growing areas in computer science that solves intractable optimization problems by emulating biologic evolution and organizational behaviors in nature. To design an EC algorithm, one needs to determine a set of algorithmic configurations like operator selections and parameter settings. How to design an effective and efficient adaptation scheme for adjusting the configurations of EC algorithms has become a significant and promising research topic in the EC research community. This paper intends to provide a comprehensive survey on this rapidly growing field. We present a classification of adaptive EC (AEC) algorithms from the perspective of how an adaptation scheme is designed, involving the adaptation objects, adaptation evidences, and adaptation methods. In particular, by analyzing the population distribution characteristics of EC algorithms, we discuss why and how the evolutionary state information of EC can be estimated and utilized for designing effective EC adaptation schemes. Two AEC algorithms using the idea of evolutionary state estimation, including the clustering-based adaptive genetic algorithm and the adaptive particle swarm optimization algorithm are presented in detail. Some potential directions for the research of AECs are also discussed in this paper.
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