| 摘要: |
| 多数遗传算法在搜索解时没有充分利用其问题域的知识.提出了一类新的改进的适应度函数的遗传算法.它考虑了函数在搜索点的函数值及其变化率,并将该信息加入适应度函数,使得按概率选择的染色体不但具有较小的函数值(对极小化问题而言),而且具有较大的函数值变化率.实验结果表明,这类方法的收敛速度明显高于标准遗传算法. |
| 关键词: SGA(标准遗传算法) GMGA(梯度改进的遗传算法) DGMGA(离散的梯度改进遗传算法) 适应度函数 优化 |
| DOI： |
| 分类号: |
| 基金项目: |
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| Genetic Algorithms Using Gradients of Object Functions |
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HE Xin gui,LIANG Jiu zhen
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| Abstract: |
| Most genetic algorithms do not use the knowledge in the related problem fields completely when searching the approximate solutions. A new kind of genetic algorithm with modified fitness functions the presented in this paper. In this algorithms, both the function value at the searching point and the function change rate at the point are combined into fitness functions. It makes the chromosome code chosen by probability be able to have both smaller function value (for minimum problem) and higher function change rate. The experimental results show that the new algorithm is convergent much faster than the standard genetic algorithm is. |
| Key words: SGA （standard genetic algorithm） GMGA （gradient modified genetic algorithm） DGMGA （discrete gradient modified genetic algorithm） fitness function optimization |
