DOI QR코드

DOI QR Code

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

Abstract

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.

Keywords

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

References

  1. Hong K. Lee, D.-H. Lee, Z. Ran, G. Lee, and M. Lee, "On Parameter Selection for Reducing Premature Convergence of Genetic Algorithms," in Proceeding of 23rd International Conference on Computer and Their Applications in Industry and Engineering, Las Vegas: NV, pp. 214-219, Nov. 2010.
  2. Hong K. Lee, "On Sweeping Operator for Reducing Premature Convergence of Genetic Algorithms," Journal of Institute of Control, Robotics and Systems, Vol. 17, No. 12, pp. 1210-1218, Dec. 2011. DOI: http://dx.doi.org/10.5302/J.ICROS.2011.17.12.1210 https://doi.org/10.5302/J.ICROS.2011.17.12.1210
  3. Hong K. Lee, "Hybrid Genetic Operators of Hamming Distance and Fitness for Reducing Premature Convergence," The Journal of Korea Navigation Institute, Vol. 18, No. 2, pp. 170-177, Apr. 2014. DOI: http://dx.doi.org/10.12673/jant.2014.18.2.170 https://doi.org/10.12673/jant.2014.18.2.170
  4. D. Goldberg and J. Richardson, "Genetic algorithms with sharing for multimodal function optimization," in Proceeding of the 2nd International Conference on Genetic Algorithms and their Applications, Hilsdale, NJ, pp. 41-49, July, 1987.
  5. S.-H. Bae and B.-R. Moon, "Mutation rates in the context of hybrid genetic algorithms", in Proceeding of the Genetic and Evolutionary Computation Conference, Seattle: WA, pp. 381-382, June, 2004.
  6. M. Srinivas and M. Patnaik, "Adaptive probabilities of crossover and mutation in genetic algorithms," IEEE Trans. on Systems, Man and Cybernetics, Vol. 24, No. 4. pp. 656-667, Apr. 1994. DOI: http://dx.doi.org/10.1109/21.286385 https://doi.org/10.1109/21.286385
  7. W. Cedeno, V. Vemuri, and T. Slezak, "Multi-niche crowding in genetic algorithms and its application to the assembly of DNA restriction-fragments," Journal of Evolutionary Computation, Vol. 2, No. 4, pp. 321-345, 1995. DOI: http://dx.doi.org/10.1162/evco.1994.2.4.321 https://doi.org/10.1162/evco.1994.2.4.321
  8. B. Sareni and L. Krahenbuhl, "Fitness Sharing and Niching Methods Revisited," IEEE Trans. on Evolutionary Computation, Vol. 2 No. 3, pp. 97-101, Sept. 1998. DOI: http://dx.doi.org/10.1109/4235.735432 https://doi.org/10.1109/4235.735432