Performance Improvement of Genetic Algorithms through Fusion of Queen-bee Evolution into the Rank-based Control of Mutation Probability

등급기준 돌연변이 확률조절에 여왕벌진화의 융합을 통한 유전자알고리즘의 성능 향상

  • Received : 2012.02.29
  • Accepted : 2012.06.21
  • Published : 2012.07.25

Abstract

This paper proposes a fusion method of the queen-bee evolution into the rank-based control of mutation probability for improving the performances of genetic algorithms. The rank-based control of mutation probability which showed some performance improvements than the original method was a method that prevented individuals of genetic algorithms from falling into local optimum areas and also made it possible for the individuals to get out of the local optimum areas if they fell into there. This method, however, showed not good performances at the optimization problems that had a global optimum located in a small area regardless of the number of local optimum areas. We think that this is because the method is insufficient in the convergence into the global optimum, so propose a fusion method of the queen-bee evolution into this method in this paper. The queen-bee evolution inspired by reproduction process of queen-bee is a method that can strengthen the convergency of genetic algorithms. From the extensive experiments with four function optimization problems in order to measure the performances of proposed method we could find that the performances of proposed method was considerably good at the optimization problems whose global optimum is located in a small area as we expected. Our method, however, showed not good performances at the problems whose global optima were distributed in broad ranges and even showed bad performances at the problems whose global optima were located far away. These results indicate that our method can be effectively used at the problems whose global optimum is located in a small area.

Acknowledgement

Supported by : 한성대학교

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