• The KIPS Transactions:PartB
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• v.17B no.2
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• pp.149-156
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• 2010

Since the premature convergence phenomenon of genetic algorithms (GAs) degrades the performances of GAs significantly, solving this problem provides a lot of effects to the performances of GAs. In this paper, we propose a selective mutation method in order to improve the performances of GAs by alleviating this phenomenon. In the selective mutation, individuals are additionally mutated at the specific region according to their ranks. From this selective mutation, individuals with low ranks are changed a lot and those with high ranks are changed small in the phenotype. Finally, some good individuals search around them in detail and the other individuals have more chances to search new areas. This results in enhancing the performances of GAs through alleviating of the premature convergence phenomenon. We measured the performances of our method with four typical function optimization problems. It was found from experiments that our proposed method considerably improved the performances of GAs.

• 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.