An empirical investigation of an evolutionary algorithm's ability to maintain a known good solution

We analyze the disruptiveness of four operators in an evolutionary algorithm(EA) solving a grammar induction problem. The EA in question contains mutation, crossover, inversion, and substitution operators. Grammars are encoded on genotypes in a representation which includes variable-length introns. A repeated measures analysis of variance (ANOVA) with four factors, the four operators' rates, is used. It is discovered that crossover and mutation rates interact, meaning that their effects on the EA's performance when used together are more than the sum of their individual effects. This suggests that operators should be studied in combination, instead of in isolation.