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A parallel reinforcement computing model for function optimization problems

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5 Author(s)
Qian, F. ; Fac. of Eng., Hiroshima Kokusai Gakuin Univ., Hiroshima, Japan ; Ikebou, S. ; Kusunoki, T. ; Wu, J.
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Learning Automaton is a learning model with outstanding learning ability, autonomy and guaranteed convergence in the 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 the traditional reinforcement method using learning automata are: increased difficulty of the adjustment of learning parameters and that of convergence time, with an increased output number. To overcome these problems, we introduce a genetic algorithm (GA) to construct a search space with reduced dimension to search for the optimal output from the entire output space, and provide an efficient way of searching for 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

Published in:

Systems, Man, and Cybernetics, 2000 IEEE International Conference on  (Volume:3 )

Date of Conference:

2000