Fuzzy genetic algorithm and applications
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Cited by (83)
Hybrid genetic algorithms in agent-based artificial market model for simulating fan tokens trading
2024, Engineering Applications of Artificial IntelligenceA robust support vector regression with exact predictors and fuzzy responses
2021, International Journal of Approximate ReasoningCitation Excerpt :The methods of fuzzy regression analysis have been suggested for linear and non-linear models. The methodologies of linear models can be classified as (1) possibilistic approaches (see for example [8–17,19,21]), (2) fuzzy least squares and fuzzy least absolutes methods (see for example [22–37]), and (3) machine learning techniques like evolutionary algorithms [38–43], SVMLs [44–47] and neural networks embedded in fuzzy regression analysis [43,48–52]. The first class of the methodologies attempts to minimize a linear/non-linear programming model by minimizing the total spread of its fuzzy parameters to support the observations at some specific levels.
A fuzzy additive regression model with exact predictors and fuzzy responses
2020, Applied Soft Computing JournalCitation Excerpt :The fuzzy regression analysis methods have been suggested for linear and nonlinear models. The linear modeling can be practiced through either (1) possibilistic approaches (see for example [10–21]), (2) fuzzy least-squares and fuzzy least-absolutes methods, or (3) evolutionary algorithms [22–29], support vector machines [30–33] and neural networks embedded in fuzzy regression analysis [34–41]. The first class of these methodologies is used to minimize a linear/nonlinear programming model by minimizing the total spread of its fuzzy parameters to support the observations at some specific levels.
Fuzzy spline univariate regression with exact predictors and fuzzy responses
2020, Journal of Computational and Applied MathematicsFuzzy regression analysis: Systematic review and bibliography
2019, Applied Soft Computing JournalCitation Excerpt :There are some papers in fuzzy regression analysis, that use evolutionary algorithms and genetic algorithms/programming. Buckley and Hayashi [182] propose fuzzy genetic algorithms to (approximately) solve fuzzy optimization problems, such as fuzzy regression. Yabuuchi and Watada [183] consider genetic algorithms in the framework of a robust fuzzy regression model (see Section 7.1).
Application of soft computing techniques to multiphase flow measurement: A review
2018, Flow Measurement and Instrumentation