Improvement of Individual Definition in Grammatical Evolution of Symbolic Regression Problem

Grammatical Evolution is one of the evolutionary computations which can find the function representation by using the one-dimensional gene in Genetic Algorithm and the translation rule described in Backus Naur Form (BNF). This paper describes the improved algorithms of Grammatical Evolution (GE) for symbolic regression problem. The present Grammatical Evolution uses two-dimensional gene, instead of the one-dimensional gene employed in the traditional GE. The linear and nonlinear functions are taken as the numerical examples. The results show that Grammatical Evolution with one-dimensional gene can find the linear function faster than the Genetic Programming and that the advanced Grammatical Evolution with two-dimensional gene is more effective than that with one-dimensional gene for finding the nonlinear function.