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

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.

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► This paper presents the optimization of fourteen fuzzy rules for semi expert judgment automation in software project management.
► The goal of this optimization is to reduce linguistic terms complexity and improve accuracy of the fuzzy rule set.
► The optimization yield more reliable early activity duration estimation result from the analysis of 33 case studies.
► It managed to reduce linguistic terms complexity by 27.76% and improve accuracy by 14.29%.
► It is aspired to complement inexperienced project managers to handle unprecedented early activity duration estimation.