Adaptation of neural agent in dynamic environment: Hybrid system of genetic algorithm and neural network

The purpose of this study is to propose the adaptive agent as hybrid of Genetic Algorithm and Neural Network, and to clarify the effectiveness of the combination of two mechanisms in the dynamic environment. Evolution and learning can be explained as the mechanism of searching a solution in the enormous possibilities at the population level and individual level respectively. Genetic algorithm and neural network are computational models. Genetic algorithm is suitable for global search, and neural network at the local search. Combination of genetic algorithm and neural network seems natural from the biological viewpoint. There are two ways of combination of genetic algorithm and neural network, that is Darwinian and Lamarckian framework. In Lamarckian framework the acquired traits during the lifetime can be passed on to the offspring directly, and in Darwinian framework, these cannot be passed on. We propose `Neural Agent' whose initial weights of their neural networks are determined by their genome data, as a simple model of hybrid system of genetic algorithm and neural network. We examine which framework is better in the dynamic system. The result of our simulation shows Darwinian framework is better than Lamarckian.