On Naïve Crossover Biases with Reproduction for Simple Solutions to Classification Problems

Abstract. A series of simple biases to the selection of crossover points in tree-structured genetic programming are investigated with respect to the provision of parsimonious solutions. Such a set of biases has a minimal computational overhead as they are based on information already used to estimate the fitness of individuals. Reductions to code bloat are demonstrated for the real world classification problems investigated. Moreover, bloated solutions provided by a uniform crossover operator often appear to defeat the application of MAPLE ™ simplification heuristics.