On self-adaptive multi-population genetic algorithms

Abstract

Multi-population genetic algorithms (MGAs), extensions of traditional single-population genetic algorithms (SGAs), have been recognized as being more effective both in speed and solution quality than SGAs. Despite of these advantages, the behavior and performance of MGAs, like SGAs, are still heavily affected by an appropriate choice of parameters such as connection topology, migration method, population number, migration interval, etc. In the past few years, though some researchers have investigated schemes for automating the parameter settings for MGAs, no work, to our knowledge, has ever investigated self-adaptation in MGAs in a systematic way. In this paper, we survey the previous work and categorize the self-adaptation of MGAs into three aspects. According to this classification, we introduce a systematic research roadmap for investigating the self-adaptation of MGAs.