GP-DMD: a genetic programming variant with dynamic management of diversity

Abstract

The proper management of diversity is essential to the success of Evolutionary Algorithms. Specifically, methods that explicitly relate the amount of diversity maintained in the population to the stopping criterion and elapsed period of execution, with the aim of attaining a gradual shift from exploration to exploitation, have been particularly successful. However, in the area of Genetic Programming, the performance of this design principle has not been studied. In this paper, a novel Genetic Programming method, Genetic Programming with Dynamic Management of Diversity (GP-DMD), is presented. GP-DMD applies this design principle through a replacement strategy that combines penalties based on distance-like functions with a multi-objective Pareto selection based on accuracy and simplicity. The proposed general method was adapted to the well-established Symbolic Regression benchmark problem using tree-based Genetic Programming. Several state-of-the-art diversity management approaches were considered for the experimental validation, and the results obtained showcase the improvements both in terms of mean square error and size. The effects of GP-DMD on the dynamics of the population are also analyzed, revealing the reasons for its superiority. As in other fields of Evolutionary Computation, this design principle contributes significantly to the area of Genetic Programming.