Learning classifier system for graph coloring

In this paper the role of genetic algorithms and classifiers systems in the graph coloring application was considered. A crossover derivative was developed to accommodate the graph coloring specifications. Also, a method for verifying and validating the new offspring generated via genetic action conforming to the graph coloring rules and regulations was considered. Through a series of experiments conducted on randomly generated graphs of a relatively small size, the importance of various parameters and their impact to the overall system performance is demonstrated and discussed. In conclusions, suggestions for future study regarding this topic are proposed and briefly discussed.