The Introduction of a Heuristic Mutation Operator to Strengthen the Discovery Component of XCS

The extended classifier systems (XCS) by producing a set of rules is (classifier) trying to solve learning problems as online. XCS is a rather complex combination of genetic algorithm and reinforcement learning that using genetic algorithm tries to discover the encouraging rules and value them by reinforcement learning. Among the important factors in the performance of XCS is the possibility to discover rules that are not only general as possible but highly Accurate. In this paper, a new mutation operator is introduced for XCS that in addition to increasing the speed of learning, will help improve performance. The purpose of speed is the amount of time that takes for the system to reach an appropriate solution and the purpose of the performance is the quality of solution that has been developed. The proposed algorithm was named XCS-KF and to evaluate its performance, it is used to solve the common problem in this area that is known as the multiplexer. The results obtained showed that the speed and performance of the proposed algorithm to XCS algorithm increased significantly.

Keywords:

extended classifier systems , Genetic Algorithms , discovery component , mutation operator , Multiplexer