The KIPS Transactions:PartB (정보처리학회논문지B)

Korea Information Processing Society (한국정보처리학회)
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Selective Mutation for Performance Improvement of Genetic Algorithms

유전자알고리즘의 성능향상을 위한 선택적 돌연변이

  • 정성훈 (한성대학교 정보통신공학과)
  • Received : 2009.09.23
  • Accepted : 2010.01.01
  • Published : 2010.04.30
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Abstract

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.

유전자알고리즘의 조숙수렴현상(premature convergence phenomenon)은 유전자알고리즘의 성능을 크게 저하시키기 때문에 이 문제를 해결하는 것이 성능향상에 크게 영향을 준다. 본 논문에서는 유전자알고리즘의 조숙수렴현상을 완화하여 성능을 향상시키기 위한 선택적 돌연변이 방법을 제안한다. 선택적 돌연변이에서는 유전자알고리즘 개체의 등급에 따라서 염색체의 특정영역에 비트를 추가적으로 돌연변이 시킨다. 이렇게 함으로서 등급이 낮은 개체는 표현형 상에서 많은 변화가 일어나고 등급이 높은 개체는 작은 변화가 일어나게 된다. 결국 좋은 개체는 그 주변을 세부적으로 탐색하며 좋지 못한 개체는 새로운 영역을 탐색할 기회가 높아지게 되어 조숙수렴현상을 완화하면서 성능향상을 꾀할 수 있게 된다. 성능향상을 측정하기 위하여 4개의 대표적 함수 최적화 문제에 적용해서 제안한 방법의 성능을 측정하였다. 실험결과 기존의 유전자알고리즘보다 성능이 크게 향상됨을 확인하였다.

Keywords

References

  1. D. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning,” Addison-Wesley, 1989.
  2. M. Srinivas and L. M. Patnaik, “Genetic Algorithms: A Survey,” IEEE Computer Magazine, pp.17-26, June, 1994. https://doi.org/10.1109/2.294849
  3. J. L. R. Filho and P. C. Treleaven, “Genetic-Algorithm Programming Environments,” IEEE Computer Magazine, pp.28-43, June, 1994. https://doi.org/10.1109/2.294850
  4. D. Beasley, D. R. Bull, and R. R. Martin, “An Overview of Genetic Algorithms: Part 1, Fundamentals,” Technical Report obtained from http://home.ifi.uio.no/~jimtoer/GA_Overview1.pdf.
  5. D. B. Fogel, “An Introduction to Simulated Evolutionary Optimization,” IEEE Transactions on Neural Networks, Vol.5, pp.3-14, Jan., 1994. https://doi.org/10.1109/72.265956
  6. H. Szczerbicka and M. Becker, “Genetic Algorithms: A Tool for Modelling, Simulation, and Optimization of Complex Systems,” Cybernetics and Systems: An International Journal, Vol.29, pp.639-659, Aug., 1998. https://doi.org/10.1080/019697298125461
  7. R. Yang and I. Douglas, “Simple Genetic Algorithm with Local Tuning: Efficient Global Optimizing Technique,” Journal of Optimization Theory and Applications, Vol.98, pp.449-465, Aug., 1998. https://doi.org/10.1023/A:1022697719738
  8. C. Xudong, Q. Jingen, N. Guangzheng, Y. Shiyou, and Z. Mingliu, “An Improved Genetic Algorithm for Global Optimization of Electromagnetic Problems,” IEEE Transactions on Magnetics, Vol.37, pp.3579-3583, Sept., 2001. https://doi.org/10.1109/20.952666
  9. J. A. Vasconcelos, J. A. Ramirez, R. H. C. Takahashi, and R. R. Saldanha, “Improvements in Genetic Algorithms,” IEEE Transactions on Magnetics, Vol.37, pp.3414-3417, Sept., 2001. https://doi.org/10.1109/20.952626
  10. E. Alba and B. Dorronsoro, “The exploration/exploitation tradeoff in dynamic cellular genetic algorithms,” IEEE Transactions on Evolutionary Computation, Vol.9, pp.126-142, Apr., 2005. https://doi.org/10.1109/TEVC.2005.843751
  11. V. K. Koumousis and C. Katsaras, “A saw-tooth genetic algorithm combining the effects of variable population size and reinitialization to enhance performance,” IEEE Transactions on Evolutionary Computation, Vol.10, pp.19-28, Feb., 2006. https://doi.org/10.1109/TEVC.2005.860765
  12. J. Andre, P. Siarry, and T. Dognon, “An improvement of the standard genetic algorithm fighting premature convergence in continuous optimization,” Advances in engineering software, Vol.32, No.1, pp.49-60, 2001. https://doi.org/10.1016/S0965-9978(00)00070-3
  13. M. Srinivas and L. M. Patnaik, “Adaptive Probabilities of Crossover and Mutation in Genetic Algorithms,” IEEE Transactions on Systems, Man and Cybernetics, Vol.24, No.4, pp.656-667, Apr., 1994. https://doi.org/10.1109/21.286385
  14. A. E. Eiben, Z. Michalewicz, m. Schoenauer, and J. E. Smith “Parameter Control in Evolutionary Algorithms,” Studies in Computational Intelligence, Vol.54, pp.19-46, 2007. https://doi.org/10.1007/978-3-540-69432-8_2
  15. Silja Meyer-Nieberg and Hans-Georg Beyer, “Self-Adaptation in Evolutionary Algorithms,” Studies in Computational Intelligence, Vol.54, pp.47-75, 2007. https://doi.org/10.1007/978-3-540-69432-8_3
  16. C. W. Ho, K. H. Lee, and K. S. Leung, “A Genetic Algorithm Based on Mutation and Crossover with Adaptive Probabilities,” Proceedings of the 1999 Congress on Evolutionary Computation, Vol.1, pp.768-775, 1999.
  17. Zhihua Tang, Youtuan Zhu, Guo Wei, and Jinkang Zhu, “An Elitist Selection Adaptive Genetic Algorithm for Resource Allocation in Multiuser Packet-based OFDM Systems,” Journal of Communications, Vol. 3, No. 3, pp.27-32, July 2008.