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

Chin Hooi Tan, Keem Siah Yap, Hwa Jen Yap

Research output: Contribution to journalArticle

14 Citations (Scopus)

Abstract

Genetic 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.

Original languageEnglish
Pages (from-to)2168-2177
Number of pages10
JournalApplied Soft Computing Journal
Volume12
Issue number8
DOIs
Publication statusPublished - 01 Aug 2012

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Fuzzy rules
Automation
Genetic algorithms
Linguistics
Knowledge based systems
Fuzzy systems
Project management
Fuzzy logic

All Science Journal Classification (ASJC) codes

  • Software

Cite this

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Application of genetic algorithm for fuzzy rules optimization on semi expert judgment automation using Pittsburg approach. / Tan, Chin Hooi; Yap, Keem Siah; Yap, Hwa Jen.

In: Applied Soft Computing Journal, Vol. 12, No. 8, 01.08.2012, p. 2168-2177.

Research output: Contribution to journalArticle

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