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    Abstract:

    Premature convergence and low converging speed are the distinct weaknesses of the genetic algorithms. Using the living things' growth pattern for reference, a new model called ECM(ecological competition model) is proposed, in which the competition is considered to be in important position. In the ECM model, the congenital genetic evolution and the postnatal competition learning on individuals' level are realized in each sub-population, moreover, the competition reinforcement learning on population level is realized. The experimental results show the ECM model's effectiveness.

    Reference
    1  Goldberg D E. Genetic Algorithms in Search, Optimization and Machine Learning. Reading, MA: Addison-Wesley Publishing Company, 1989 2  Rudolph G. Convergence analysis of canonical genetic algorithms. IEEE Transactions on Neural Networks, 1994,5(1):96~101 3  Schradoph N N et al. Dynamic parameter encoding for genetic algorithms. Machine Learning, 1992,9(1):9~21 4  Srinivas M et al. Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Transactions on Systems, Man and Cybernetics, 1994,24(4):656~667 5  Cohoon J P et al. A multi-population genetic algorithm for solving the k-partition problem on hyper-cubes. In: Belew R K ed. Proceedings of the 4th International Conference on Genetic Algorithms. San Mateo, CA: Morgan Kaufmann Publisher, Inc., 1991. 244~248 6  Yural Davidor. An ecological model for evolutionary computing. システム/制御/情报, 1993,31(8):468~474 (Yural Davidor. An ecological model for evolutionary computing. System/Control/Information, 1993,31(8):468~474) 7  Rumelhart D E et al. Explorations in Parallel Distributed Processing. Cambridge, MA: MIT Press, 1988 8  De Jong. An analysis of the behavior of a class of genetic adaptive system [Ph.D. Thesis]. University of Michigen, 1975 9  陈国良,王煦法等.遗传算法及其应用.北京:人民邮电出版社,1996 (Chen Guo-liang, Wang Xu-fa et al. Genetic Algorithms and Its Applications. Beijing: People's Post and Telecommunications Publishing House, 1996)
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曹先彬,高 隽,王煦法.基于生态竞争模型的遗传强化学习.软件学报,1999,10(6):658-662

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History
  • Received:April 20,1998
  • Revised:June 29,1998
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