Abstract
In this paper, we will propose a novel framework of hybridization of Coevolutionary Genetic Algorithm and Machine Learning. The Coevolutionary Genetic Algorithm (CGA) which has already been proposed by Handa et al. consists of two GA populations: the first GA (H-GA) population searches for the solutions in given problems, and the second GA (P-GA) population searches for effective schemata of the H-GA. The CGA adopts the notion of commensalism, a kind of co-evolution. The new hybrid framework incorporates a schema extraction mechanism by Machine Learning techniques into the CGA. Considerable improvement in its search ability is obtained by extracting more efficient and useful schemata from the H-GA population and then by incorporating those extracted schemata into the P-GA. We will examine and compare two kinds of machine learning techniques in extracting schema information: C4.5 and CN2. Several computational simulations on multidimensional knapsack problems, constraint satisfaction problems and function optimization problems will reveal the effectiveness of the proposed methods.
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