Anisotropic selection in cellular genetic algorithms

David Simoncini (1) , Sébastien Verel (1) , Philippe Collard (1) , Manuel Clergue (1)

1 I3S - Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis

In this paper we introduce a new selection scheme in cellular genetic algorithms (cGAs). Anisotropic Selection (AS) promotes diversity and allows accurate control of the selective pressure. First we compare this new scheme with the classical rectangular grid shapes solution according to the selective pressure: we can obtain the same takeover time with the two techniques although the spreading of the best individual is different. We then give experimental results that show to what extent AS promotes the emergence of niches that support low coupling and high cohesion. Finally, using a cGA with anisotropic selection on a Quadratic Assignment Problem we show the existence of an anisotropic optimal value for which the best average performance is observed. Further work will focus on the selective pressure self-adjustment ability provided by this new selection scheme.

Keywords: evolutionary computation; genetic algorithm; cellular automata; parallel model; selective pressure

Domains: Artificial Intelligence [cs.AI]

David Simoncini, Sébastien Verel, Philippe Collard, Manuel Clergue. Anisotropic selection in cellular genetic algorithms. Genetic And Evolutionary Computation Conference, Jul 2006, Seatle, United States. pp.559 - 566, ⟨10.1145/1143997.1144098⟩. ⟨hal-00164697⟩