An analysis of depth of crossover points in tree-based Genetic Programming

The standard crossover operator in tree-based Genetic Programming (GP) is problematic in that it is most often destructive. Selecting crossover points with an implicit bias towards the leaves of a program tree aggravates its destructiveness and causes the code bloat problem in GP. Therefore, a common view has been developed that adjusting the depth of crossover points to eliminate the bias can improve GP performance, and many attempts have been made to create effective crossover operators according to this view. As there are a large number of possible depth-control strategies, it is very difficult to identify the strategy that provides the most significant improvement in performance. This paper explores depth-control strategies by analysing the depth of crossover points in evolutionary process logs of five different GP systems on problems in three different domains. It concludes that controlling the depth of crossover points is an evolutionary stage dependent and problem dependent task, and obtaining a significant performance improvement is not trivial.