An evolution of cellular automata neural systems using DNA coding method

Cellular Automata Neural Systems (CANS) are neural networks based on biological development and evolution. Each neuron of CANS has local connections and acts as a form of pulse, according to the dynamics of the chaotic neuron model. CANS are generated from initial cells according to the CA rule. In the previous study, to obtain the useful ability of CANS, we make the pattern of initial cells evolve. However, it is impossible to represent all solution space, so we propose an evolving method of CA rule to overcome this defect in this paper. DNA coding has the redundancy and overlapping of gene and is apt for the representation of the rule. In this paper, we show the general expression of CA rule and propose translating method from DNA code to CA rule. In addition, we propose effective evolution method by considering the characteristics of DNA coding method. The effectiveness of the proposed scheme is verified by applying it to the navigation problem of autonomous mobile robot.