| 求解多目标优化问题的改进遗传机器学习方法 |
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| 引用本文： | 刘明姬,刘淑媛,吕显瑞. 求解多目标优化问题的改进遗传机器学习方法[J]. 吉林大学学报(理学版), 2006, 44(3): 338-342 |
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| 作者姓名： | 刘明姬 刘淑媛 吕显瑞 |
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| 作者单位： | 吉林大学,数学学院,长春,130012;吉林大学,数学学院,长春,130012;吉林商业高等专科学校,长春,130015 |
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| 摘 要： | 将遗传算法与机器学习相结合, 在分类器系统的基础上, 引入增强因子、 排挤因子、 合并因子等改进因子, 完善信度分配机制, 提出了改进的遗传机器学习方法. 并将算法应用于投资的收益与风险双目标优化模型, 数值结果表明, 改进算法能够寻求到数量更多、 分布更广的Pareto最优解, 并且具有较好的稳定性, 避免了非成熟收敛.
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| 关 键 词： | 遗传算法 机器学习 多目标优化 Pareto最优解 |
| 文章编号： | 1671-5489(2006)03-0338-05 |
| 收稿时间： | 2005-05-25 |
| 修稿时间： | 2005-05-25 |
Improved Genetic Algorithm Based on Machine Learning for Solving Multi-objective Optimization Problems |
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| LIU Ming-ji,LIU Shu-yuan,L Xian-rui. Improved Genetic Algorithm Based on Machine Learning for Solving Multi-objective Optimization Problems[J]. Journal of Jilin University: Sci Ed, 2006, 44(3): 338-342 |
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| Authors: | LIU Ming-ji LIU Shu-yuan L Xian-rui |
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| Affiliation: | 1. College of Mathematics, Jilin University, Changchun 130012, China; 2. Jilin Commercial College, Changchun 130015, China |
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| Abstract: | An improved genetic algorithm based on machine learning is presented. In the improved algorithm we make genetic algorithm and machine learning proceed alternatively with the assistance of improved factors. For solving multi-objective optimization problems, the improved algorithm can find more and wider Pareto- optimal solutions, with more stability, convergence, global search and without premature convergence. |
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| Keywords: | genetic algorithm machine learning multi-objective optimization Pareto-optimal solution |
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