一种基于遗传算法多维模糊分类器的构造方法

 提出了一种新的模糊遗传机器学习方法.在该方法中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;在获取模糊规则的同时,还对每个属性维的模糊划分进行学习;另外,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,从而很大程度改善了遗传算法收敛到局部解的问题.经实验结果表明,该方法在多维模糊分类器的构造问题上具有比较良好的性能.

A new approach on construction of fuzzy classifier system for multid imensional pattern classification using genetic algorithms

It is proposed a new approach based on fuzzy genetics-based machine learning mechanism.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 and keep the diversity of population,and avoid the premature convergence problem consequently.Finally the experiments prove that the approach works very well on multidimensional pattern classification problems.