| 摘要: |
| 针对经典遗传算法在优化计算中存在的弊端,提出进化遗传算法.在染色体基因位编码方式、交叉与变异算子、适合度函数设计、候选解染色体选取方式及收敛准则等方面做了改进.通过对极难优化的Shekel类函数等的优化计算,说明该算法具有良好的全局搜索能力和较快的收敛速度. |
| 关键词: 遗传算法,Shekel类函数,优化计算,进化策略,收敛准则. |
| DOI： |
| 分类号: |
| 基金项目:本文研究得到国家自然科学基金、国家863高科技项目基金、中国科学院机器人学开放研究实验室基金和德国科学联合会DFG基金资助. |
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| Optimization Computing Based on Evolution Genetic Algorithm |
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CHEN Ming
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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. |
| Key words: Genetic algorithm, Shekel' function, optimization computing, evolution storage, convergence criterion. |
