Abstract
In this paper, we address a solution to density classification tasks using knowledge-based genetic algorithms. Cellular automata (CAs) are used as models of self -organization and emergent computation, and known to have capacity to solve complex problems. It is, however, very difficult to design transition rules that respond to the user's requests, and it prevents the practical application of CAs. Therefore automatic generation of transition rules is studied. We propose a new method to obtain transition rules using knowledge-based genetic algorithms. The knowledge here is a candidate partial solution of the final solution. As a result of infection, the genes of a partial solution are substituted for those of an individual. The purpose of this study is to obtain rules faster than traditional methods. We use the majority decision rule for the knowledge. Experimental results for density classification tasks prove that the proposed method is faster than a conventional method. In addition, the evidence is given that the best transition rules emerge by the partial evolution of the majority decision rule.
