Improvement of the GA's Convergence Speed Using the Sub-Population

보조 모집단을 이용한 유전자 알고리즘의 수렴속도 개선

  • Lee, Hong-Kyu (School of Electrical, Electronics and Communication Engineering, Korea University of Technology and Education) ;
  • Lee, Jae-Oh (School of Electrical, Electronics and Communication Engineering, Korea University of Technology and Education)
  • 이홍규 (한국기술교육대학교 전기전자통신공학부) ;
  • 이재오 (한국기술교육대학교 전기전자통신공학부)
  • Received : 2014.07.03
  • Accepted : 2014.10.10
  • Published : 2014.10.31


Genetic Algorithms (GAs) are efficient methods for search and optimization problems. On the other hand, there are some problems associated with the premature convergence to local optima of the multimodal function, which has multi peaks. The problem is related to the lack of genetic diversity of the population to cover the search spaces sufficiently. A sharing and crowding method were introduced. This paper proposed strategies to improve the convergence speed and the convergence to the global optimum for solving the multimodal optimization function. These strategies included the random generated sub-population that were well-distributed and spread widely through search spaces. The results of the simulation verified the effects of the proposed method.


Convergence Speed;Genetic Diversity;Genetic Operator;Global Optimum;Multimodal Function


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