A perspective on the foundation and evolution of the linkage learning genetic algorithms

Intelligent guessing plays a critical role in the success and scalability of a non-enumerative optimization algorithm that primarily relies on the samples taken from the search space to guide the optimization process. Linkage learning deals with the issue of intelligent guessing by exploiting properties of the representation. This paper underscores the importance of linkage learning in genetic algorithms and other adaptive sampling-based optimization algorithms. It develops the foundation, identifies the problems of implicit linkage learning in simple genetic algorithms, reviews some of the early linkage learning efforts, reports some of the recent developments, and identifies the future directions of linkage learning research.

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