Genetic learning for combinational logic design

Abstract

This paper investigates the effect of injection percentage on the performance of a case-injected genetic algorithm for combinational logic design. A case-injected genetic algorithm is a genetic algorithm augmented with a case-based memory of past problem solving attempts which learns to improve performance on sets of similar design problems. In this approach, 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 a parity checker, 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.