TWEANN Approach to the Double Pole Balancing Problem

— Feature Comparison Between NEAT and MBEANN

Abstract

We discuss the effect of mutation probabilities on the performance of Mutation-Based Evolving Artificial Neural Networks (MBEANN), which is one of the methods of Topology and Weight Evolving Artificial Neural Networks (TWEANN). TWEANN is an approach for evolving both structures and weights of artificial neural networks. TWEANN is expected to perform well than an approach using a fixed-topology neural network with only evolving weight values. The phenotype of MBEANN consists of sub-networks, and the topology of the neural network grows independently within them. Moreover, the structural mutations of MBEANN are designed to reduce the influence on the fitness value. In this study, we focus on the effect of structural mutation probabilities on performance by using a double pole balancing problem without velocity inputs. The performance of MBEANN is compared with NeuroEvolution of Augumenting Topologies (NEAT), which is a typical method of TWEANN. The results show that MBEANN has a higher task achievement rate regardless of the mutation probabilities and task difficulty. From the comparison with NEAT, MBEANN shows a higher performance even with a larger network structure due to the phenotype that consists of sub-networks.