• Journal of the Korea Society of Computer and Information
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• v.17 no.4
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• pp.129-137
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• 2012

The queen-bee genetic algorithm that we made by mimicking of the reproduction of queen-bee has considerably improved the performances of genetic algorithm. However, since we used only one queen-bee in the queen-bee genetic algorithm, a problem that individuals of genetic algorithm were driven to one place where the queen-bee existed occurred. This made the performances of the queen-bee genetic algorithm degrade. In order to solve this problem, we introduce a multiple queen-bee evolution method by employing another queen-bee whose fitness is the most significantly increased than its parents as well as the original queen-bee that is the best individual in a generation. This multiple queen-bee evolution makes the probability of falling into local optimum areas decrease and allows the individuals to easily get out of the local optimum areas even if the individuals fall into a local optimum area. This results in increasing the performances of the genetic algorithm. Experimental results with four function optimization problems showed that the performances of the proposed method were better than those of the existing method in the most cases.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.4
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• pp.263-268
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• 2010

The premature convergence of genetic algorithms (GAs) is the most major factor of slow evolution of GAs. In this paper we propose a novel method to solve this problem through competition of multiple offspring of in dividuals. Unlike existing methods, each parents in our method generates multiple offspring and then generated multiple offspring compete each other, finally winner offspring become to real offspring. From this multiple offspring competition, our GA rarel falls into the premature convergence and easily gets out of the local optimum areas without negative effects. This makes our GA fast evolve to the global optimum. Experimental results with four function optimization problems showed that our method was superior to the original GA and had similar performances to the best ones of queen-bee GA with best parameters.