EVOLUTIONARY ROBOT BEHAVIORS BASED ON NATURAL SELECTION AND NEURAL NETWORK

摘要

The methodology of artificial evolution based on the traditional fitness function is argued to be inadequate for constructing the entities with behaviors novel to their designers. Evolutionary emergence via natural selection(without an explicit fitness function) is a promising way. This paper primarily considers the question of what to evolve, and focuses on the principles of developmental modularity based on neural networks. The connection weight values of this neural network are encoded as genes, and the fitness individuals are determined using a genetic algorithm. In paper we has created and described an artificial world containing autonomous organisms for developing and testing some novel ideas. Experimental results through simulation have demonstrated that the developmental system is well suited to long-term incremental evolution. Novel emergent strategies are identified both from an observer's perspective and in terms of their neural mechanisms.