Learning Automaton Computing of Function Optimization Problems

Abstract

Learning Automaton (LA) is a representative model with the properties of outstanding learning ability, autonomy and theoretically guaranteed convergence in learning process[1]. But for function optimization problems, the problematic point is with the increase of solution space, the output number of stochastic automaton also increases, therefore convergence is possibly very time-consuming.

For alleviating the problem, in present paper, we introduce Genetic Algorithm (GA) to existing LA. According to GA, a searching space is constructed to look for the optimal output from the entire output space and the way of searching for the optimal output from the smaller-sized searching space is observed. To verify the efficacy, for multi-variable function optimization problem under parallel environment, the parallel picture of this method is drawn.