Mutational Robustness and Structural Complexity in Grammatical Evolution

A recent study in Artificial Life found that the need for mutational robustness can give rise to simpler structures in an evolving population. This begs the question, do we observe a similar phenomenon in Genetic Programming? Genetic Programming requires the search of structural space of solutions, usually requiring code growth to find fitter solutions. Typically Genetic Programming algorithms then suffer from code bloat, which is code growth in the absence of fitness gains. In this study we ask a simple question. Would the necessity for mutational robustness under selection pressure drive the evolution of less complex solution structures in Genetic Programming, with the potential to counteract code bloat?

I. Introduction

With some exceptions, biological complexity is considered to have increased over evolutionary time [1]. The causes of increased complexity are theorised to be either a function of selection pressure or due to the operation of the variation operators, with degeneracy (a form of biological redudancy) being a key feature [2]. In a recent study in the Artifical Life literature [3], in the presence of selection, the generation of increasing complexity is observed in an environment where functional simplicity has higher fitness. However, with increased rates of mutation the complexity of the agents is observed to reduce. That is, the necessity for mutational robustness can give rise to simpler structures.