Optimization Computing Based on Evolution Genetic Algorithm
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    Abstract:

    Deeply analyzed the conventional genetic algorithm and for its shortcomings on numerical optimization, evolution genetic algorithm (EGA) is proposed. EGA makes some improvements on description of genes of chromosome, genetic operators of crossover and mutation, designing of fitness function, selection method on chromosome of candidate solutions and convergence criterion. The optimization results of some functions (including hard Shekel' function) show that EGA has fine ability of global searching and speedy convergence.

    Reference
    1  Holland J H. Adaptive of Natural and Artificial Systems. Ann Arbor: The University of Michigan Press, 1975 2  Goldberg D. Genetic Algorithms in Search, Optimization and Machine Learning. Reading, MA: Addison Wesley, 1989
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陈 明.基于进化遗传算法的优化计算.软件学报,1998,9(11):876-879

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History
  • Received:March 28,1997
  • Revised:October 21,1997
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