Contribution based bloat control in Genetic Programming

Andy Song, Dunhai Chen, Mengjie Zhang

Abstract: Unnecessary growth in program size is known as the bloat problem in Genetic Programming. Bloat not only increases computational expenses during evolution, but also impairs the understandability and execution performance of evolved final solutions. There are a large number of studies addressing this problem. In this paper, we present an effective bloat control mechanism which is based on examining the contribution of each function node in the selected programs. Nodes without contribution will be removed before generating offspring. This method has been applied to various tasks. The results show that it can significantly reduce program size without damping the fitness of individuals. In some cases it increases the performance of the final solutions. Furthermore it does not require extra computational resources to perform the control whilst it speeds up evolution processes because of the saving in evaluation costs.