Case injected genetic algorithms for learning across problems

Abstract

Genetic algorithms (GAs) augmented with a case-based memory of past design problem-solving attempts are used to obtain better performance over time on sets of similar design problems. Rather than starting anew on each design, a GA's population is periodically injected with appropriate intermediate design solutions to similar, previously solved design problems. Experimental results on configuration design problems: the design of parity checker and adder circuits, demonstrate the performance gains from the approach and show that the 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.

Keywords:

• Learning
• Similarity
• Genetic algorithm

Acknowledgements

This material is based in part upon work supported by the National Science Foundation under Grant No. 9624130 and by contract number N00014-03-1-0104 from the Office of Naval Research.