On the optimal convergence probability of univariate estimation of distribution algorithms
Evolutionary Computation
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Parallel algorithms are presented for modules of learning automata with the objective of improving their speed of convergence without compromising accuracy. A general procedure suitable for parallelizing a large class of sequential learning algorithms on a shared memory system is proposed. Results are derived to show the quantitative improvements in speed obtainable using parallelization. The efficacy of the procedure is demonstrated by simulation studies on algorithms for common payoff games, parametrized learning automata and pattern classification problems with noisy classification of training samples