Exploring the Explorative Advantage of the Cooperative Coevolutionary (1+1) EA

Thomas Jansen^1* and R. Paul Wiegand²

¹FB 4, LS2, Univ. Dortmund,
44221 Dortmund, Germany
Thomas.Jansen@udo.edu

²Krasnow Institute,
George Mason University,
Fairfax, VA 22030
paul@tesseract.org

Abstract. Using a well-known cooperative coevolutionary function optimization framework, a very simple cooperative coevolutionary (1+1) EA is defined. This algorithm is investigated in the context of expected optimization time. The focus is on the impact the cooperative coevolutionary approach has and on the possible advantage it may have over more traditional evolutionary approaches. Therefore, a systematic comparison between the expected optimization times of this coevolutionary algorithm and the ordinary (1+1) EA is presented. The main result is that separability of the objective function alone is is not sufficient to make the cooperative coevolutionary approach beneficial. By presenting a clear structured example function and analyzing the algorithms' performance, it is shown that the cooperative coevolutionary approach comes with new explorative possibilities. This can lead to an immense speed-up of the optimization.

^*The research was partly conducted during a visit to George Mason University. This was supported by a fellowship within the post-doctoral program of the German Academic Exchange Service (DAAD).

LNCS 2723, p. 310 ff.

Full article in PDF

lncs@springer.de
© Springer-Verlag Berlin Heidelberg 2003