| 摘要: |
| 讨论了基于模糊遗传机器学习机制的密歇根方法在多维分类问题上的应用及性能问题,并提出了一种新的模糊遗传学习方法.将每一模糊规则作为遗传算法中的一个个体,且具有相应的适应度函数值.在提取模糊规则的同时,还对每个属性维的模糊划分进行学习以获取较好的模糊集合参数.另外,该方法引入了基于相似性的选择机制,减轻了选择机制对低适应函数值个体造成的选择压力,保持了种群的多样性,从而有效地避免了遗传算法收敛到局部解的问题.实验结果表明,该方法在多维模糊分类器的构造问题上具有较高的正确分类率、适应性较好等性能. |
| 关键词: 模糊遗传学习机制 密歇根方法 模糊分类器 早熟收敛 精英选择 基于相似性的选择 |
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| 基金项目:Supported by the National Natural Science Foundation of China under Grant No.60266003(国家自然科学基金);the Youth Natural Science Foundation of Yunnan University under Grant No.2004Q027C(云南大学青年自然科学基金) |
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| Research on the Construction of Fuzzy Classifier System for Multidimensional Pattern Classification Using Genetic Algorithms |
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LI Ji-Dong,ZHANG Xue-Jie
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| Abstract: |
| This paper discusses the application and performance of multidimensional pattern classification problems using Michigan approach based on fuzzy genetics-based machine learning mechanism, and proposes a new approach. In the approach, each fuzzy if-then rule is handled as an individual, and a fitness value is assigned to it. The approach not only retrieves fuzzy if-then rules, but also tunes the membership functions of each dimension, meanwhile the selection mechanism based on the similarity of individuals is involved to reduce the high selective pressure, keep the diversity of population, and avoid the premature convergence problem consequently. Finally the experiments prove that the approach has a better correct classification rate and a better adaptability on multidimensional pattern classification problems. |
| Key words: fuzzy genetic-based machine learning mechanism Michigan approach fuzzy classifier premature convergence elist selection selection mechanism based on similarity |
