An analysis of explicit loops in genetic programming

In this paper we analyse the reasons why evolving programs with a restricted form of loops is superior to evolving programs without loops for two problems which have underlying repetitive characteristics - a visit-every-square problem and a modified Santa Fe ant problem. We show that in the case of loops there is a larger number of solutions with smaller tree sizes. We show that the computational patterns captured in the bodies of the loops are reflective of repeating patterns in the domain. We show that the increased computational cost of evaluating an individual can be controlled by domain knowledge.