An investigation of the mutation operator using different representations in Grammatical Evolution

Grammatical evolution (GE) is a form of grammar-based genetic programming. A particular feature of GE is that it adopts a distinction between the genotype and phenotype similar to that which exists in nature by using a grammar to map between the genotype and phenotype. This study seeks to extend our understanding of GE by examining the impact of different genotypic representations in order to determine whether certain representations, and associated diversity-generation operators, improve GE’s efficiency and effectiveness. Four mutation operators using two different representations, binary and gray code representation respectively, are investigated. The differing combinations of representation and mutation operator are tested on three benchmark problems. The results provides support for the continued use of the standard genotypic integer representation as the alternative representations do not exhibit higher locality nor better GE performance. The results raise the question as to whether higher locality in GE actually improves GE performance.