Case Base Solutions C a ses Preprocessor Genetic Algorithm GA Module CBR Module

This paper presents a new approach to genetic algorithm based machine learning. We use genetic algorithms augmented with a case-based memory of past problem solving attempts to obtain better performance over time on sets of similar problems. Rather than starting anew on each problem, we periodically inject a genetic algorithm's population with appropriate intermediate solutions to similar, previously solved problems. Using simple syntactic similarity measures, our experimental results on a connguration design problem, and on a path planning problem demonstrate the ro-bustness of our approach. These results show that our system learns to take less time to provide a solution to a new problem as it gains experience from solving other similar problems { exactly what we want from a learning system.