The E ect of Extensive Use of the MutationOperator on Generalization in GeneticProgramming using Sparse Data

Ordinarily, Genetic Programming uses little or no mutation. Crossover is the predominant operator. This study tests the eeect of a very aggressive use of the mutation operator on the generalization performance of our Compiling Genetic Programming System ('CPGS'). We ran our tests on two benchmark classiication problems on very sparse training sets. In all, we performed 240 complete runs of population 3000 for each of the problems, varying mutation rate between 5% and 80%. We found that increasing the mutation rate can signiicantly improve the generalization capabilities of GP. The mechanism by which mutation aaects the generalization capability of GP is not entirely clear. What is clear is that changing the balance between mutation and crossover eeects the course of GP training substantially | for example, increasing mutation greatly extends the number of generations for which the GP system can train before the population converges.