Examining the landscape of semantic similarity based mutation

This paper examines how the semantic locality of a search operator affects the fitness landscape of Genetic Programming (GP). We compare the fitness landscapes of GP search when standard subtree mutation and a recently proposed semantic-based mutation, Semantic Similarity-based Mutation (SSM), are used. The comparison is based on two well-studied fitness landscape measures, namely, the autocorrelation function and information content. The experiments were conducted on a family of symbolic regression problems with increasing degrees of difficulty. The results show that SSM helps to significantly smooth out the fitness landscape of GP compared to standard subtree mutation. This gives an explanation for the better performance of SSM over standard subtree mutation operator.