Rule-based integration of multiple neural networks evolved based on cellular automata

There has been extensive research into developing a controller for a mobile robot. Especially, several researchers have constructed a mobile robot controller that can avoid obstacles, evade predators, or catch moving prey by evolutionary algorithms such as genetic algorithms and genetic programmings. In this line of research, we presented a method of applying CAM-Brain, an evolved neural network based on cellular automata, to control a mobile robot. However, this approach has limitations when making the robot perform appropriate behavior in complex environments. In this paper, we have attempted to solve this problem by combining several modules evolved to do simple behavior by a rule-based approach. Experimental results show that this approach has possibility for developing a sophisticated neural controller for complex environments.