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| 摘要: |
| 针对遗传算法效率低等问题，基于复杂系统理论对其作了以下改进:首先，用反映复杂系统能量分布的幂律法则改造了选择算子;其次，引入复杂系统自学习特性重新设计了交叉算子;再次，采用反馈机理改进了更新策略;最后，在算法中增加了基因漂流算子。通过上述改造，复杂系统遗传算法各个算子相互平衡、相互制约，有效地抑制了遗传算法的“早熟”，并在很大程度上提高了算法的效率。进一步通过实验结果表明，该算法在高维优化中具有较好的性能。 |
| 关键词: 遗传算法 复杂系统 幂律法则 基因漂流 |
| DOI： |
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| 基金项目:Supported by the National Natural Science Foundation of China under Grant No.50705073 (国家自然科学基金); the Shaanxi Provincial Natural Science Foundation of China under Grant No.2006E224 (陕西省自然科学基金) |
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| High Efficient Complex System Genetic Algorithm |
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ZHUANG Jian, YANG Qing-Yu, DU Hai-Feng, YU De-Hong
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| Abstract: |
| In order to overcome the problems of genetic algorithm, such as the low efficiency, genetic algorithm is redesigned by the complex system theory in this paper. First, the selecting operator is rebuilt by the power law, which is considered to be the self-organized criticality of complex system and sound distribution system of energy. Second, the crossover operator is redesigned by the characteristic of a self-learning complex system. Third, the generation strategy is improved by the mechanism of feedback. Finally, the gene floating operator is added to the algorithm. Because all operators are balanced with each other and restrict each other, the newly designed algorithm, complex system genetic algorithm (CSGA), improves efficiency and premature markedly. At last, experiments show that the CSGA is capable of dealing with high dimensional global optimization problems. |
| Key words: genetic algorithm complex system power law gene floating |
