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

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