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An Incremental and Non-generational Coevolutionary Algorithm
Ramón Alfonso Palacios-Durazo1 and Manuel Valenzuela-Rendón2
1Lumina Software,
apd@luminasoftware.com
Washington 2825 Pte C.P. 64040,
Monterrey N.L., Mexico
http://www.luminasoftware.com/apd
2ITESM, Monterrey
Centro de Sistemas Inteligentes
valenzuela@itesm.mx,
C.P. 64849 Monterrey, N.L., Mexico
http://www-csi.mty.itesm.mx/~mvalenzu
Abstract. The central idea of coevolution lies in the fact that the fitness of an individual depends on its performance against the current individuals of the opponent population. However, coevolution has been shown to have problems [2, 5]. Methods and techniques have been proposed to compensate the flaws in the general concept of coevolution [2]. In this article we propose a different approach to implementing coevolution, called incremental coevolutionary algorithm (ICA) in which some of these problems are solved by design. In ICA, the importance of the coexistance of individuals in the same population is as important as the individuals in the opponent population. This is similar to the problem faced by learning classifier systems (LCSs) [1, 4]. We take ideas from these algorithms and put them into ICA.
LNCS 2723, p. 371 f.
lncs@springer.de
© Springer-Verlag Berlin Heidelberg 2003