A fuzzy cooperative genetic algorithm for fuzzy modeling

Fuzzy modeling refers to the process of identifying the fuzzy parameters by defining fuzzy rules and fuzzy sets. The process of identifying the fuzzy parameters becomes complicated and difficult to evaluate particularly when it involves complex problems in engineering and medical applications. This...

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Main Author:	Ismail, Mohd. Arfian
Format:	Thesis
Language:	English
Published:	2011
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/32804/1/MohdarfianIsmailMFSKSM2011.pdf
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id	my-utm-ep.32804
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spelling	my-utm-ep.328042018-05-27T07:54:58Z A fuzzy cooperative genetic algorithm for fuzzy modeling 2011-05 Ismail, Mohd. Arfian QA75 Electronic computers. Computer science Fuzzy modeling refers to the process of identifying the fuzzy parameters by defining fuzzy rules and fuzzy sets. The process of identifying the fuzzy parameters becomes complicated and difficult to evaluate particularly when it involves complex problems in engineering and medical applications. This is due to the fact that there are many fuzzy parameters to be identified such as the characteristics of the fuzzy rules and fuzzy sets. Besides that, the accuracy and the ability to interpret the fuzzy rules and the fuzzy sets would also need to be considered. This study proposes an improved method of fuzzy modeling called the Fuzzy Cooperative Genetic Algorithm (FCoGA) which integrates the Genetic Algorithm (GA) and Cooperative Coevolution Algorithm (CooCEA) that automatically generate and refine the fuzzy rules and the fuzzy sets. The GA is used to exploit the chromosomes that represent the fuzzy parameters whereas the CooCEA is applied to reduce the complexity of the fuzzy parameters representation which is carried out by subdividing chromosomes into three sub-chromosomes known as species. The FCoGA comprises of three phases which are simplification, tuning and evaluation. Simplification phase involves decomposition of the chromosomes into three species of chromosomes that represent the fuzzy parameters consisting of fuzzy rules, membership functions and length of the overlapping membership functions. The tuning phase involves the process of altering and tuning all the species. Lastly, the evaluation phase validates the performance of the FCoGA. Three benchmark datasets; breast cancer, diabetes and Iris have been used to evaluate the performance of the FCoGA. The experimental results showed that the FCoGA obtained the highest percentage of accuracy classification compared to other techniques such as conventional GA, multi-objective CooCEA, rule extraction and decision tree. The results also indicated that the FCoGA produced higher interpretability of a fuzzy model. 2011-05 Thesis http://eprints.utm.my/id/eprint/32804/ http://eprints.utm.my/id/eprint/32804/1/MohdarfianIsmailMFSKSM2011.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Computer Science and Information System Faculty of Computer Science and Information System
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Ismail, Mohd. Arfian A fuzzy cooperative genetic algorithm for fuzzy modeling
description	Fuzzy modeling refers to the process of identifying the fuzzy parameters by defining fuzzy rules and fuzzy sets. The process of identifying the fuzzy parameters becomes complicated and difficult to evaluate particularly when it involves complex problems in engineering and medical applications. This is due to the fact that there are many fuzzy parameters to be identified such as the characteristics of the fuzzy rules and fuzzy sets. Besides that, the accuracy and the ability to interpret the fuzzy rules and the fuzzy sets would also need to be considered. This study proposes an improved method of fuzzy modeling called the Fuzzy Cooperative Genetic Algorithm (FCoGA) which integrates the Genetic Algorithm (GA) and Cooperative Coevolution Algorithm (CooCEA) that automatically generate and refine the fuzzy rules and the fuzzy sets. The GA is used to exploit the chromosomes that represent the fuzzy parameters whereas the CooCEA is applied to reduce the complexity of the fuzzy parameters representation which is carried out by subdividing chromosomes into three sub-chromosomes known as species. The FCoGA comprises of three phases which are simplification, tuning and evaluation. Simplification phase involves decomposition of the chromosomes into three species of chromosomes that represent the fuzzy parameters consisting of fuzzy rules, membership functions and length of the overlapping membership functions. The tuning phase involves the process of altering and tuning all the species. Lastly, the evaluation phase validates the performance of the FCoGA. Three benchmark datasets; breast cancer, diabetes and Iris have been used to evaluate the performance of the FCoGA. The experimental results showed that the FCoGA obtained the highest percentage of accuracy classification compared to other techniques such as conventional GA, multi-objective CooCEA, rule extraction and decision tree. The results also indicated that the FCoGA produced higher interpretability of a fuzzy model.
format	Thesis
qualification_level	Master's degree
author	Ismail, Mohd. Arfian
author_facet	Ismail, Mohd. Arfian
author_sort	Ismail, Mohd. Arfian
title	A fuzzy cooperative genetic algorithm for fuzzy modeling
title_short	A fuzzy cooperative genetic algorithm for fuzzy modeling
title_full	A fuzzy cooperative genetic algorithm for fuzzy modeling
title_fullStr	A fuzzy cooperative genetic algorithm for fuzzy modeling
title_full_unstemmed	A fuzzy cooperative genetic algorithm for fuzzy modeling
title_sort	fuzzy cooperative genetic algorithm for fuzzy modeling
granting_institution	Universiti Teknologi Malaysia, Faculty of Computer Science and Information System
granting_department	Faculty of Computer Science and Information System
publishDate	2011
url	http://eprints.utm.my/id/eprint/32804/1/MohdarfianIsmailMFSKSM2011.pdf
_version_	1747816064506396672

A fuzzy cooperative genetic algorithm for fuzzy modeling

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