Coevolution in recurrent neural networks using genetic algorithms
Yuji Sato, Tatsumi Furuya · Systems and Computers in Japan · 1996
Abstract This paper describes an investigation into the effectiveness of a lookahead model based on recurrent neural networks. An action network and an internal model of the environment are incorporated into the recurrent neural network; lookahead planning is performed while configuring the action network through learning in the internal model. A genetic algorithm is applied to the design of the neural networks. The effectiveness of this model is evaluated by applying it to the game of “tic‐tac‐toe,” and the following result is obtained. It is possibly more effective to perform learning in the internal model by learning algorithms than by memorizing input‐output correspondences.