Optimization of Fuzzy Inference System Using Genetic Algorithm

Veerababu, Seela Naga; Roja, Konna; Reddy, M. V. Kumar; Reddy, K. Manoz Kumar; Adireddy, Ramesh

doi:10.1007/978-981-15-7394-1_21

Cited by 2 publications

(1 citation statement)

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“…analyzed and optimized fuzzy inference with a GAbased approach that reduced rules and computation time and complexity. The result is that Mamdani-based FIS has been optimized using GA for control system applications [23]. Lipiński et.al.…”

Section: Related Workmentioning

confidence: 99%

Enhancing the fuzzy inference system using genetic algorithm for predicting the optimum production of a scientific publishing house

Kushadiani

Buono²,

Nugroho

2022

Comput Sci Inf Technol

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As a scientific publishing house, Indonesian Institute of Sciences (LIPI) Press' encountered some problems in publication planning, mainly predicting the optimum production of publications. This study aimed to enhance a fuzzy inference system (FIS) parameters using the genetic algorithm (GA). The enhancements led to optimally predict the number of LIPI Press publications for the following year. The predictors used were the number of work units, the number of workers, and the publishing process duration. The dataset covered a five years range of total production of LIPI Press. Firstly, an expert set up the parameters of the fuzzy inference system denoted as a FIS expert. Next, we performed a FIS GA by applying the genetic algorithm and K-fold validation in splitting the training data and testing data. The FIS GA revealed optimum prediction with parameters that were composed of both population size (30), the probability of crossover (0.75), the probability of mutation (0.01), and the number of generations (150). The experiment results show that our enhanced FIS GA outperformed FIS expert approach.

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Section: Related Workmentioning

confidence: 99%