An Incremental and Non-generational Coevolutionary Algorithm

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. In a coevolutionary algorithm, the fitness landscape depends on the opponent population, therefore it changes every generation. The individuals selected for reproduction are those more promising to perform better against the fitness landscape represented by the opponent population. However, if the complet