Comparative with Adaptive GP-Automata and GNP for Aliasing Problems

Abstract

In this paper, we examine the performance of genetic network programming (GNP) for learning agents on perceptual aliasing problems (PAPs). In order to cope with this problem, a genetic programming approach called Adaptive Genetic-Programming Automata has been already proposed. While it effectively tackled to PAPs, too many rules are generated that are not used to control the agent. Simulation results clearly show that the number of rules is reduced by GNP in a maze problem in which a learning agent tries to reach a goal.