Rank-based Semantic Control Crossover in Genetic Programming

Subtree exchange crossover which is usually used in Genetic Programming (GP) can not control the search properties such as global or local search, because crossover points in parental individuals are selected at random. To overcome the problem, crossover based on semantic distance of subtrees has been studied recent years. If similar subtrees in semantic space are exchanged, the local search can be performed. In contrast, dissimilar subtrees are exchanged, the global search can be performed. In Semantic Control Crossover (SCC), the global search can be performed in early generations, and the local search can be performed in later generations. In this paper, we propose a new SCC based on the ranking information of parents, Rank-based SCC. The method controls search properties according to not generations but ranking information of parents. In case of the crossover to a pair of parents with higher ranks, similar subtrees should be exchanged for local search around the parents. In contrast, in case of the crossover to a pair of parents with lower ranks, dissimilar subtrees should be exchanged for global search. We compared the search performance of three methods, standard crossover, conventional SCC and Rank-based SCC, and showed the effectiveness of our method.