한국지능시스템학회논문지 (Journal of the Korean Institute of Intelligent Systems)

  • 제17권7호
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  • Pages.869-874
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  • 2007
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  • 1976-9172(pISSN)
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  • 2288-2324(eISSN)
한국지능시스템학회 (Korean Institute of Intelligent Systems)
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다목적 함수 최적화를 위한 게임 모델에 기반한 공진화 알고리즘에서의 해집단의 다양성에 관한 연구

Study on Diversity of Population in Game model based Co-evolutionary Algorithm for Multiobjective optimization

  • 이희재 (중앙대학교 전자전기공학부) ;
  • 심귀보 (중앙대학교 전자전기공학부)
  • 발행 : 2007.12.25
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⟨ 이전 논문 다음 논문 ⟩

초록

다목적 함수의 최적화 문제(Multiobjective optimization problems)의 경우에는 하나의 최적해가 존재하는 것이 아니라 '파레토 최적해 집합(Pareto optimal set)'이라고 알려진 해들의 집합이 존재한다. 이러한 이상적 파레토 최적해 집합과 가까운 최적해를 찾기 위한 다양한 해탐색 능력은 진화 알고리즘의 성능을 결정한다. 본 논문에서는 게임 모델에 기반한 공진화 알고리즘(GCEA: Game model based Co-Evolutionary Algorithm)에서 해집단의 다양성을 유지하여, 다양한 비지배적 파레토 대안해(non-dominated alternatives)들을 찾기 위한 방법을 제안한다.

In searching for solutions to multiobjective optimization problem, we find that there is no single optimal solution but rather a set of solutions known as 'Pareto optimal set'. To find approximation of ideal pareto optimal set, search capability of diverse individuals at population space can determine the performance of evolutionary algorithms. This paper propose the method to maintain population diversify and to find non-dominated alternatives in Game model based Co-Evolutionary Algorithm.

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참고문헌

  1. K. Deb and D. E. Goldberg, 'An investigation of niche and species formation in genetic function optimization,' Proceedings of the Third International Conference on Genetic Algorithms, J.D.Schaffer, Ed. San Mateo, CA:Morgan Kauffman, pp. 42-50, 1989
  2. T .Blickle and L. Thiele, 'A comparison of selection schemes used in evolutionary algorithms,' Evol. Comput, vol. 4, no. 4, pp.361-394, 1996 https://doi.org/10.1162/evco.1996.4.4.361
  3. E. Zitzler, 'Multiobjective evolutionary algorithms : the strength pareto approach,' IEEE Trans. Evolutionary Computation, vol. 3, no. 4, 1999
  4. N. Srinivas and K. Deb, 'Multiobjective optimization using nondominated sorting in genetic algorithm,' Evol. Comput, vol. 2, no. 3, pp. 221-248, 1994 https://doi.org/10.1162/evco.1994.2.3.221
  5. J. D. Knowles and D. W. Come, 'Approximating the nondominated front using the pareto archived evolution strategy,' Evol. Comput, vol. 8, no. 2, pp. 149-172, 2000 https://doi.org/10.1162/106365600568167
  6. M. Laumanns, L. Thiele, K. Deb, E. Zitzler, 'Combining convergence and diversity in evolutionary multiobjective optimization,' Evol. Comput, vol. 10, no. 3, pp. 263-282, 2002 https://doi.org/10.1162/106365602760234108
  7. P. Czyzak and A. Jaszkiewicz, 'Pareto simulated annealing - A metaheuristic for multiobjective combinatorial optimization, ' Multi-Criteria Decision Anal, vol. 7, pp. 34-47, 1998 https://doi.org/10.1002/(SICI)1099-1360(199801)7:1<34::AID-MCDA161>3.0.CO;2-6
  8. J. Horn and N. Nafpliotis and D. E. Goldberg, 'A niched pareto genetic algorithm for multiobjective opti- mization,' Proceeding of the First IEEE Conference on Evolutionary Computation, Picataway, NJ : IEEE Press, pp. 82-87, 1994
  9. S. G. Ficici and J. B. Pollack, 'A Game Theoretic Memory Mechanism for Coevolution,' GECCO 2003, LNCS 2723, pp. 286-297, 2003
  10. K. B. Sim and J. Y. Kim, 'Game Theory Based Co-evolutionary Algorithm,' KFIS2004, vol. 14, No.3, pp 253-261, 2004
  11. M. Sefrioui and J. Periaux, 'Nash Genetic Algorithms : examples and applications,' Proc. of the 2000 Congress on Evolutionary Computation CEC00, IEEE Press, pp. 509-516, 2000
  12. J. D. Schaffer, 'Multiple objective optimization with vector evaluated genetic algorithm,' Proceedings of an International Conference on Genetic Algorithms and their Applications, pp. 93-100, 1985
  13. E. Zitzler and L, Thiele, 'Multiobjective evolutionary algorithms - a comparative case study,' Fifth International Conference on Parallel Problem Solving from Nature(PPSN-V), Springer, Berlin, Germany, pp. 292-301, 1998
  14. E. Zitzler, K. Deb, and L. Thiele, 'Comparison of multi-objective evolutionary algorithms: Empirical results,' Evol. Comput, vol. 8, no. 2, pp. 173-195, 2000 https://doi.org/10.1162/106365600568202
  15. K. Deb, S. Agrawal, A. Pratap, and T. Meyarivan, 'A fast elitist non-dominated sorting genetic algorithm for multi -objective optimization : NSGA-II,' Parallel Problem Solving from Nature(PPSN VI), M. Schoenauer et al., Eds. Berlin, Germany : Springer, pp. 849-858, 2002