A survey of linkage learning techniques in genetic and evolutionary algorithms
15 April 2007Chen, Y.-p., Yu, T.-L., Sastry, K., Goldberg, D. E. (2007). IlliGAL Report No. 2007014. University of Illinois at Urbana-Champaign, Urbana IL. [Full paper - PDF] [Full paper - PS].
Abstract:
This paper reviews and summarizes existing linkage learning techniques for genetic and evolutionary algorithms in the literature. It first introduces the definition of linkage in both biological systems and genetic algorithms. Then, it discusses the importance for genetic and evolutionary algorithms to be capable of learning linkage, which is referred to as the relationship between decision variables. Existing linkage learning methods proposed in the literature are reviewed according to different facets of genetic and evolutionary algorithms, including the means to distinguish between good linkage and bad linkage, the methods to express or represent linkage, and the ways to store linkage information. Studies related to these linkage learning methods and techniques are also investigated in this survey.
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