In order to overcome the poor behaviors of genetic algorithms in some problems other classes of evolutionary algorithms have been recently developed by researchers. Although these algorithms do not have the simplicity of classic genetic algorithms but they are superior More
In order to overcome the poor behaviors of genetic algorithms in some problems other classes of evolutionary algorithms have been recently developed by researchers. Although these algorithms do not have the simplicity of classic genetic algorithms but they are superior to genetic algorithms. The Probabilistic Model Building Genetic Algorithms or Estimation of Distribution Algorithms (EDAs) is one of these classes which is recently developed. In this paper we introduce a new estimation of distribution algorithm based on Learning Automata. The proposed algorithm is a model based search optimization method that uses a set of learning automata as a probabilistic model of the population of solutions in the search space. The proposed algorithm is a simple algorithm which has produced good results for the optimization problems considered in this problem.
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In this paper, a method based on learning automata for adaptation of the vigilance factor in Fuzzy ARTMAP network when used for classification problems is proposed. The performance of the proposed algorithm is independent of the initial value for vigilance factor. Fuzz More
In this paper, a method based on learning automata for adaptation of the vigilance factor in Fuzzy ARTMAP network when used for classification problems is proposed. The performance of the proposed algorithm is independent of the initial value for vigilance factor. Fuzzy ARTMAP network in which the vigilance factor adapted using learning automata generates smaller structure with higher recognition rate. To study the performance of the proposed method it has been applied to several problems: circle in square, spirals and square in square problems. The results of experiments show the effectiveness of the proposed method.
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