Genetic Fuzzimetric Technique (GFT)

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dc.contributor.author Kouatli, Issam
dc.contributor.editor Yager, Ronald R. en_US
dc.contributor.editor Sgurev, Vassil S. en_US
dc.contributor.editor Hajiski, Mincho B. en_US
dc.date.accessioned 2017-11-10T13:48:23Z
dc.date.available 2017-11-10T13:48:23Z
dc.date.copyright 2012 en_US
dc.date.issued 2017-11-10
dc.identifier.isbn 9.78147E+12 en_US
dc.identifier.uri http://hdl.handle.net/10725/6585 en_US
dc.description.abstract Integration of fuzzy systems with genetic algorithm has been identified by researchers as a useful technique of optimizing systems under uncertainty. This integration is usually referred to as Genetic Fuzzy systems (GFS) where different researchers adopted different techniques to achieve the functionality of GFS. This paper proposes another new methodology based on the concept of Fuzzimetric Arcs which is also reviewed in this paper. This new proposed technique is termed as Genetic Fuzzimetric Technique (GFT) where the strength of this technique is based on the systematic approach of defining fuzzy sets (variables), cross-over and mutation of these variables in order to find the optimized performance. Most of real life decision making processes are of that type of uncertainty and hence the need of a fuzzy system that can be optimized. One such problem is to decide on the expected performance level of the student during the admission process to the university. This example was taken as a vehicle to clarify the mechanism of GFT. en_US
dc.language.iso en en_US
dc.publisher IEEE en_US
dc.subject Intelligent control systems -- Congresses en_US
dc.subject Expert systems (Computer science) -- Congresses en_US
dc.subject Artificial intelligence -- Congresses en_US
dc.title Genetic Fuzzimetric Technique (GFT) en_US
dc.type Conference Paper / Proceeding en_US
dc.title.subtitle A New Optimization Methodology Using the Concept of Fuzzimetric Arcs en_US
dc.author.school SOB en_US
dc.author.idnumber 200301034 en_US
dc.author.department Department of Information Technology and Operations Management (ITOM) en_US
dc.description.embargo N/A en_US
dc.publication.place Piscataway, N.J. en_US
dc.keywords Biological cells en_US
dc.keywords Shape en_US
dc.keywords Educational institutions en_US
dc.keywords Fuzzy systems en_US
dc.keywords Genetic algorithms en_US
dc.keywords Fuzzy sets en_US
dc.keywords Tuning en_US
dc.description.bibliographiccitations Includes bibliographical references and author index en_US
dc.identifier.doi http://dx.doi.org/10.1109/IS.2012.6335135 en_US
dc.identifier.ctation Kouatli, I. (2012, September). Genetic Fuzzimetric Technique (GFT): A new optimization methodology using the concept of Fuzzimetric Arcs. In Intelligent Systems (IS), 2012 6th IEEE International Conference (pp. 194-199). IEEE. en_US
dc.author.email issam.kouatli@lau.edu.lb en_US
dc.conference.date September 6-8, 2012 en_US
dc.conference.pages 194-199 en_US
dc.conference.place Sofia, Bulgaria en_US
dc.conference.subtitle proceedings en_US
dc.conference.title 2012 6th IEEE International Conference Intelligent Systems en_US
dc.identifier.tou http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php en_US
dc.identifier.url http://ieeexplore.ieee.org/abstract/document/6335135/ en_US
dc.publication.date 2012 en_US
dc.author.affiliation Lebanese American University en_US

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