Controlling crossover through inductive learning

Abstract

Crossover may achieve the fast combination of performant building blocks; but as a counterpart, crossover may as well break a newly discovered building block. We propose to use inductive learning to control such disruptive effects of crossover. The idea is to periodically gather some examples of crossovers, labelled as ”good” or ”bad” crossovers according to their effects on the current population. From these examples, inductive learning builds rules characterizing the crossover quality. This ruleset then enables to control further evolution: crossovers classified ”bad” according to the ruleset are refused. Some experimentations on the Royal Road problem are discussed.