Adaptive Representations for Improving Evolvability, Parameter Control, and Parallelization of Gene Expression Programming

Gene Expression Programming (GEP) is a genetic algorithm that evolves linear chromosomes encoding nonlinear (tree-like)structures. In the original GEP algorithm, the genome size is problem specific and is determined through trial and error. Inthis work, a method for adaptive control of the genome size is presented. The approach introduces mutation, transposition,and recombination operators that enable a population of heterogeneously structured chromosomes, something the originalGEP algorithm does not support. This permits crossbreeding between normally incompatible individuals, speciation within apopulation, increases the evolvability of the representations, and enhances parallel GEP. To test our approach, an assortment ofproblems were used, including symbolic regression, classification, and parameter optimization. Our experimental results showthat our approach provides a solution for the problem of self-a daptive control of the genome size of GEP’s representation.