Genetic learning from experience

Abstract

This paper describes a technique for combining genetic algorithm with a long term memory of past problems solving attempts to obtain better performance over time on sets of similar design problems. Rather than starting anew on each design, we periodically inject a genetic algorithm's population with appropriate intermediate design solutions to similar, previously solved problems. Experimental results on a configuration design problem; the design of an adder and circuits similar to adders, demonstrate the performance gains from our approach and show that our system learns to take less time to provide quality solutions to a new design problem as it gains experience from solving other similar design problems. We hope that this simple technique will help in implementing evolutionary computing applications in industry.