On the roles of semantic locality of crossover in genetic programming

Locality has long been seen as a crucial property for the efficiency of Evolutionary Algorithms in general, and Genetic Programming (GP) in particular. A number of studies investigating the effects of locality in GP can be found in the literature. The majority of the previous research on locality focuses on syntactic aspects, and operator semantic locality has not been thoroughly tested. In this paper, we investigate the role of semantic locality of crossover in GP. We follow McPhee in measuring the semantics of a subtree using the fitness cases. We use this to define a semantic distance metric. This semantic distance supports the design of some new crossover operators, concentrating on improving semantic locality. We study the impact of these semantically based crossovers on the behaviour of GP. The results show substantial advantages accruing from the use of semantic locality.