Hybrid Evolutionary Learning Approaches for The Virus Game

Abstract

This paper investigates the effectiveness of hybrids of learning and evolutionary approaches to find weights and topologies for an artificial neural network (ANN) which is used to evaluate board positions for a two-person zero-sum game, the virus game. Two hybrid approaches: evolutionary RPROP (resilient backpropagation) and evolutionary BP (backpropagation) are described and empirically compared with BP, RPROP, iRPROP (improved RPROP) and evolutionary learning approaches. The results show that evolutionary RPROP and evolutionary BP have significantly better generalisation performance than their constituent learning and evolutionary methods.