Modeling coevolutionary genetic algorithms on two-bit landscapes: partnering strategies

Abstract

Different from standard genetic algorithms where each individual is evaluated separately according to predefined objective function(s), one most notable characteristic of coevolutionary genetic algorithms (CGA) is that evaluation procedures require more than one individual and an individual's fitness is depending on its interactions with its partners. In consequence, the implemented partnering strategies can have significant effects on the dynamical behaviour of CGA as well as their optimization performance. Infinite population models of CGA consisting of two populations coevolving on two-bit landscapes are described and investigated in the context of four well-applied partnering strategies. It is shown that even in these simplest models, the dynamical behaviour of CGA changes dramatically according to different evolutionary scenarios that deserves our attention from the perspective of coevolutionary algorithms designing.