The Role of Syntactic and Semantic Locality of Crossover in Genetic Programming

This paper investigates the role of syntactic locality and semantic locality of crossover in Genetic Programming (GP). First we propose a novel crossover using syntactic locality, Syntactic Similarity based Crossover (SySC). We test this crossover on a number of real-valued symbolic regression problems. A comparison is undertaken with Standard Crossover (SC), and a recently proposed crossover for improving semantic locality, Semantic Similarity based Crossover (SSC). The metrics analysed include GP performance, GP code bloat and the effect on the ability of GP to generalise. The results show that improving syntactic locality reduces code bloat, and that leads to a slight improvement of the ability to generalise. By comparison, improving semantic locality significantly enhances GP performance, reduces code bloat and substantially improves the ability of GP to generalise. These results comfirm the more important role of semantic locality for crossover in GP.

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