Learning Fuzzy Rules for Modeling Complex Classification Systems using Genetic Algorithms
Genetic algorithms, as general purpose learning techniques, have been widely applied in the modeling of fuzzy rules-based classification system. However, the algorithms are more vulnerable to local convergence as a result of the increasing complexity and dimensionality of classification problems, which reduces the performance of the algorithms. To prevent the algorithms only learning rules from small subset of the search space, a fitness sharing method based on the similarity level of one rule from its neighbours rules is proposed. The similarity level is calculated by the similarity values of different antecedent fuzzy sets, which are cached for reducing the additional computing load. The proposed method is studied for two complex data, the sonar signals classification and the hand movement recognition problems. And the experimental results demonstrate that the proposed method is able to efficiently achieve accurate performance.
complex classification problems genetic fuzzy rulebased systems fitness-sharing-based learning
Ji-Dong Li Xue-Jie Zhang Yun Gao
School of Vocational and Continuing Education, Yunnan University Kunming, China School of Information Science and Engineering, Yunnan University Kunming, China
国际会议
太原
英文
576-580
2010-10-22（万方平台首次上网日期，不代表论文的发表时间）