Optimization of calibration data with the dynamic genetic algorithm
Optimization of calibration data with the dynamic genetic algorithm
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DOI：
10.1016/0003-2670(92)85255-5
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发表时间：
1992-10
影响因子：
6.2
通讯作者：
Tonghua Li;C. Lucasius;G. Kateman
中科院分区：
文献类型：
--
作者：
Tonghua Li;C. Lucasius;G. Kateman
遗传算法构成了一套功能强大的搜索启发式算法。采用改进的遗传算法对标定数据集进行优化。为了构建理想的遗传程序，种群的多样性是至关重要的。提出的思想是估计沿两个方向的多样性，即群体中染色体之间的多样性和所有染色体中等位基因之间的多样性。新定义的多样性函数能够详细描述遗传算法的过程，并且可以作为反馈，以几乎理想的方式对过程进行动态控制。优化结果表明，无论是短期运行还是长期运行，动态遗传算法都优于“经典”遗传算法，优化后不仅可以压缩和细化数据集，而且可以提高校正模型的预测能力。
Genetic algorithms constitute a set of powerful search heuristics. A modified genetic algorithm was used to optimize calibration data sets. In order to construct an ideal genetic procedure, the diversity in a population is crucial. The idea proposed is to estimate the diversities along two directions, namely the diversity between the chromosomes in a population and the diversity between the alleles in all chromosomes. The newly defined diversity functions are able to describe the procedure of a genetic algorithm in detail and can be used as a feedback for dynamic control of the process in an almost ideal way. The optimization results show that for both short and long runs the dynamic genetic algorithm is superior to the “classical” genetic algorithms and that after optimization not only can the data sets be compacted and refined but also the predictive ability of the calibration model can be improved.