Explicit Control of Diversity and Effective Variation Distance in Linear Genetic Programming

We investigate structural and semantic distance metrics for linear genetic programs. Causal connections between changes of the genotype and fitness changes form a necessary condition for analyzing structural differences between genetic programs and for the two major objectives of this paper: (i) Distance information between individuals is used to control structural diversity of population individuals actively by a two-level tournament selection. (ii) Variation distance of effective code is controlled for different genetic operators—including an effective variant of the mutation operator that works closely with the used distance metric. Numerous experiments have been performed for a regression problem, a classification task, and a Boolean problem.