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Application of genetic algorithm for fuzzy rules optimization on semi expert judgment automation using Pittsburg approach

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dc.citedby18
dc.contributor.authorTan C.H.en_US
dc.contributor.authorYap K.S.en_US
dc.contributor.authorYap H.J.en_US
dc.contributor.authorid55175180600en_US
dc.contributor.authorid24448864400en_US
dc.contributor.authorid35319362200en_US
dc.date.accessioned2023-12-29T07:46:29Z
dc.date.available2023-12-29T07:46:29Z
dc.date.issued2012
dc.description.abstractGenetic algorithm is well-known of its best heuristic search method. Fuzzy logic unveils the advantage of interpretability. Genetic fuzzy system exploits potential of optimization with ease of understanding that facilitates rules optimization. This paper presents the optimization of fourteen fuzzy rules for semi expert judgment automation of early activity based duration estimation in software project management. The goal of the optimization is to reduce linguistic terms complexity and improve estimation accuracy of the fuzzy rule set while at the same time maintaining a similar degree of interpretability. The optimized numbers of linguistic terms in fuzzy rules by 27.76% using simplistic binary encoding mechanism managed to improve accuracy by 14.29% and reduce optimization execution time by 6.95% without compromising on interpretability in addition to promote improvement of knowledge base in fuzzy rule based systems. � 2012 Elsevier B.V.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1016/j.asoc.2012.03.018
dc.identifier.epage2177
dc.identifier.issue8
dc.identifier.scopus2-s2.0-84861871169
dc.identifier.spage2168
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84861871169&doi=10.1016%2fj.asoc.2012.03.018&partnerID=40&md5=758d0fe8e5928d79c9dc3e3115eaefcf
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/30302
dc.identifier.volume12
dc.pagecount9
dc.sourceScopus
dc.sourcetitleApplied Soft Computing Journal
dc.subjectEarly activity duration estimation
dc.subjectExpert judgment
dc.subjectFuzzy rules optimization
dc.subjectGenetic algorithm
dc.subjectGenetic fuzzy system
dc.subjectPittsburg approach
dc.subjectEstimation
dc.subjectFuzzy logic
dc.subjectFuzzy rules
dc.subjectHeuristic algorithms
dc.subjectKnowledge based systems
dc.subjectLinguistics
dc.subjectOptimization
dc.subjectProject management
dc.subjectActivity duration
dc.subjectActivity-based
dc.subjectBinary encodings
dc.subjectExecution time
dc.subjectExpert judgment
dc.subjectFuzzy rule set
dc.subjectGenetic fuzzy systems
dc.subjectHeuristic search methods
dc.subjectInterpretability
dc.subjectKnowledge base
dc.subjectLinguistic terms
dc.subjectPittsburg approach
dc.subjectSimilar degree
dc.subjectSoftware project management
dc.subjectGenetic algorithms
dc.titleApplication of genetic algorithm for fuzzy rules optimization on semi expert judgment automation using Pittsburg approachen_US
dc.typeArticleen_US
dspace.entity.typePublication
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