Abstract
To construct the model of reinforcement learning systems, we presented a theoretic model of stochastic learning cellular automata (SLCA) in our previous paper. The SLCA is an extended model of traditional cellular automaton, defined as a stochastic cellular automaton with its random environment. There are three rule spaces for the SLCA: parallel , sequential and mixture. This paper suggests a parallel SLCA with a genetic operator and applies it to the combinatorial optimization problems. The computer simulations of graph partition problem show that the convergence of SLCA is better than parallel mean field algorithm.
