Combining incrementally evolved neural networks based on cellular automata for complex adaptive behaviors

There has been extensive work to construct an optimal controller for a mobile robot by evolutionary approaches such as genetic algorithm, genetic programming, and so on. However, evolutionary approaches have a difficulty to obtain the controller for complex and general behaviors. In order to overcome this shortcoming, we propose an incremental evolution method for neural networks based on cellular automata (CA) and a method of combining several evolved modules by a rule-based approach. The incremental evolution method evolves the neural network by starting with simpler environment needed simple behavior and gradually making it more complex and general for complex behaviors. The multimodules integration method can make complex and general behaviors by combining several modules evolved or programmed to do simple behavior. Experimental results show the potential of the incremental evolution and multi-modules integration methods as techniques to make the evolved neural network to do complex and general behaviors.