Removing code bloating in crossover operation in Genetic Programming

Abstract

The concept of “bloat” in Genetic Programming is a well established phenomenon characterized by variable-length genomes gradually increasing in size during evolution. Bloat is basically a problem that occurs during crossover and mutation. In this paper we are proposing a special type of crossover operation named as Fitness, Elitism, Depth limit & Size (FEDS) crossover to reduce bloat in which we are using local elitism replacement in combination with depth limit and size of the trees to reduce bloat without a subsequent loss of performance. We are also using the point mutation technique together with the FEDS crossover in order to reduce the bloat. To demonstrate our approach we have designed a Multiclass Classifier using GP by taking few benchmark datasets. The results obtained show that by applying FEDS crossover together with point mutation reduces the problem of bloat substantially without compromising the performance.