The Application Research of the Chaos Genetic Algorithm (CGA) and its Evaluation of Optimization Efficiency
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摘要: 利用混沌运动的遍历性,提出了一种求解优化问题的混沌遗传算法(CGA,Chaos Genetic A1gorithm). 该算法的基本思想是把混沌变量加载于遗传算法的变量群体中,利用混沌变 量对子代群体进行微小扰动并随着搜索过程的进行逐渐调整扰动幅度.研究结果表明,该方法 效果显著,明显提高了优化计算效率.本文将"平均截止代数"和"截止代数分布熵"作为评价指 标,对混沌遗传算法(CGA)的优化效率进行了研究,定量地评价了CGA的优化效率,通过与遗 传算法(GA)进行比较,进一步说明了CGA的优化效率高于GA.Abstract: By means of the ergodic property of chaos movement, a chaos genetic algorithm (CGA) is proposed. The basic principle of CGA is that a small disturbance is added to the child generation group by using the chaos variable and the disturbance amplitude is adjusted little by little as the search goes on. The computational results indicate that the CGA has good performance and significantly improves the computational efficiency in optimization. "The average truncated generations" and "the distribution entropy of truncated generations" are used to evaluate the optimization efficiency of CGA. The optimization efficiency of CGA is evaluated quantificationally. It is shown that the optimization efficiency of CGA is higher than that of genetic algorithm.
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Key words:
- Chaos Genetic Algorithm /
- random disturbance /
- optimization /
- optimization efficiency
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