Pre-, In- and Postfix grammars for Symbolic Regression in Grammatical Evolution

Recent research has indicated that grammar design is an important consideration when using grammar-based Genetic Programming, particularly with respect to unintended biases that may arise through rule ordering or duplication. In this study we examine how the ordering of the elements during mapping can impact performance. Here we use to the standard GE depth-first mapper and compare the performance of postfix, prefix and infix grammars on a selection of symbolic regression problem instances. We show that postfix can confer a performance advantage on the harder problems examined.