Autosoft Journal

Online Manuscript Access

A Chromosome Representation Encoding Intersection Points for Evolutionary Design of Fuzzy Classifiers



Unlike the conventional chromosome representation to search the shape of fuzzy membership functions, a novel encoding scheme to search the optimal intersection points between adjacent fuzzy membership functions is originally presented for evolutionary design of fuzzy classifiers. Since the proposed representation contains the intersection points directly related to the boundary of classification, it is intuitively expected that redundancy of the search space is reduced and the performance is better in comparison with the conventional encoding scheme. The experimental results show that the proposed encoding scheme gives superior or competitive performance in two real-world datasets and gives more interpretable fuzzy classifiers. This short paper has provided additional explanation to the previous works introduced in the latest conference.



Total Pages: 10
Pages: 237-246


Manuscript ViewPdf Subscription required to access this document

Obtain access this manuscript in one of the following ways

Already subscribed?

Need information on obtaining a subscription? Personal and institutional subscriptions are available.

Already an author? Have access via email address?


Volume: 18
Issue: 3
Year: 2012

Cite this document


O. Cordon, F. Gomide, F. Herrera, F. Hoffmann, and L. Magdalena, “Ten years of genetic fuzzy systems: current framework and new trends,” Fuzzy Sets and Systems, 141, (1), pp. 5–31, 2004.

Zhou, Enwang, and Alireza Khotanzad. "Fuzzy Classifier Design Using Genetic Algorithms." Pattern Recognition 40.12 (2007): 3401-3414. Crossref. Web.

L.I. Kuncheva, “Fuzzy classifier design,” Springer, Physica-Verl., Heidelberg, 2000.

Kim, M.-S., C.-H. Kim, and J.-J. Lee. "Evolving Compact and Interpretable Takagi-Sugeno Fuzzy Models With a New Encoding Scheme." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 36.5 (2006): 1006-1023. Crossref. Web.

Klose, A., and A. Nurnberger. "On the Properties of Prototype-Based Fuzzy Classifiers." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 37.4 (2007): 817-835. Crossref. Web.

M.S. Kim and J.J. Lee, “Design of a Fuzzy Logic Controller with Evolutionary Q-Learning,” International Journal of Intelligent Automation and Soft Computing, Vol. 5, December, 2005.

Ishibuchi, H., and T. Nakashima. "Effect of Rule Weights in Fuzzy Rule-Based Classification Systems." IEEE Transactions on Fuzzy Systems 9.4 (2001): 506-515. Crossref. Web.

Ishibuchi, Hisao, Tomoharu Nakashima, and Takehiko Morisawa. "Voting in Fuzzy Rule-Based Systems for Pattern Classification Problems." Fuzzy Sets and Systems 103.2 (1999): 223-238. Crossref. Web.

Ho, S.-Y. et al. "Design of Accurate Classifiers With a Compact Fuzzy-Rule Base Using an Evolutionary Scatter Partition of Feature Space." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 34.2 (2004): 1031-1044. Crossref. Web.

Fernández, Alberto et al. "Solving Multi-Class Problems with Linguistic Fuzzy Rule Based Classification Systems Based on Pairwise Learning and Preference Relations." Fuzzy Sets and Systems 161.23 (2010): 3064-3080. Crossref. Web.

Michalewicz, Zbigniew. "Genetic Algorithms + Data Structures = Evolution Programs." (1996): n. pag. Crossref. Web.

A. Asuncion and D.J. Newman, “UCI Machine Learning Repository [˜mlearn/MLRepository.html],” Irvine, CA: University of California, Department of Information and Computer Science, 2007.

J.Y. Lee, J.H. Seok, Y.J. Kim, and J.J. Lee, “Evolutionary Design of Fuzzy Classifiers Using Intersection Points,” Industrial Informatics (INDIN), 2010 8th IEEE International Conference on, pp. 98–101, 2010.


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)

SJR: "The two years line is equivalent to journal impact factor ™ (Thomson Reuters) metric."

Journal: 1995-Present


TSI Press
18015 Bullis Hill
San Antonio, TX 78258 USA
PH: 210 479 1022
FAX: 210 479 1048