A parallel reinforcement computing model for function optimization problems

Learning Automaton is a learning model with outstanding learning ability, autonomy and guaranteed convergence in learning process. We propose a parallel computing model with learning automata for function optimization problems and implement it as a sparse distributed parallel computing system. The problems with traditional reinforcement method using learning automata are increase of the difficulty of the adjustment of learning parameters and that of convergence time, with increase of output number. To improve them, we introduce genetic algorithm (GA) to construct a search space with reduced dimension to look for the optimal output from the entire output space, and provide an efficient way of searching the smaller-sized search space for the optimal solution. The results of computer simulations verify the usefulness of the proposed method for multivariable function optimization problems.