Genetic Programming with Epigenetic Local Search

Abstract

We focus on improving genetic programming through local search of the space of program structures using an inheritable epigenetic layer that specifies active and inactive genes. We explore several genetic programming implementations that represent the different properties that epigenetics can provide, such as passive structure, phenotypic plasticity, and inheritable gene regulation. We apply these implementations to several symbolic regression and program synthesis problems. For the symbolic regression problems, the results indicate that epigenetic local search consistently improves genetic programming by producing smaller solution programs with better fitness. Furthermore, we find that incorporating epigenetic modification as a mutation step in program synthesis problems can improve the ability of genetic programming to find exact solutions. By analyzing population homology we show that the epigenetic implementations maintain diversity in silenced portions of programs which may provide protection from premature convergence.