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

  • Authors:

  • Chin Hooi Tan;Keem Siah Yap;Hwa Jen Yap

  • Affiliations:

  • College of Information Technology, Universiti Tenaga Nasional, Malaysia;College of Graduate Studies, Universiti Tenaga Nasional, Malaysia;Department of Engineering Design and Manufacture, Faculty of Engineering, University of Malaya, Malaysia

  • Venue:
  • Applied Soft Computing
  • Year:
  • 2012

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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.