Hybrid Harmony Search and Genetic for Fuzzy Classification Systems


Authors

Maryam Sadat Mahmoodi - Department of Computer, Payame Noor University, I.R of IRAN. Seyed Abbas Mahmoodi - Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Yazd, Iran.


Abstract

In this paper, a method based on Harmony Search Algorithm (HSA) is proposed for pattern classification. One of the important issues in the design of fuzzy classifier if the product of fuzzy if then rules. So that the number of incorrectly classified patterns is minimized. In the HSA-based method, every musician makes a musical note and it can be regarded as a solution vector. The algorithm uses Genetic algorithm based local search to improve the quality of fuzzy classification system. The proposed algorithm is evaluated on a breast cancer data. The results show that the algorithm based on improved genetic is able to produce a fuzzy classifier to detect breast cancer.


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ISRP Style

Maryam Sadat Mahmoodi, Seyed Abbas Mahmoodi, Hybrid Harmony Search and Genetic for Fuzzy Classification Systems, Journal of Mathematics and Computer Science, 10 (2014), no. 3, 203-211

AMA Style

Mahmoodi Maryam Sadat, Mahmoodi Seyed Abbas, Hybrid Harmony Search and Genetic for Fuzzy Classification Systems. J Math Comput SCI-JM. (2014); 10(3):203-211

Chicago/Turabian Style

Mahmoodi, Maryam Sadat, Mahmoodi, Seyed Abbas. "Hybrid Harmony Search and Genetic for Fuzzy Classification Systems." Journal of Mathematics and Computer Science, 10, no. 3 (2014): 203-211


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