Performance evaluation of evolutionary multiobjective optimization algorithms for multiobjective fuzzy genetics-based machine learning

Recently, evolutionary multiobjective optimization (EMO) algorithms have been utilized for the design of accurate and interpretable fuzzy rule-based systems. This research area is often referred to as multiobjective genetic fuzzy systems (MoGFS), where EMO algorithms are used to search for non-domin...

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Vydané v:	Soft computing (Berlin, Germany) Ročník 15; číslo 12; s. 2415 - 2434
Hlavní autori:	Ishibuchi, Hisao, Nakashima, Yusuke, Nojima, Yusuke
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Berlin/Heidelberg Springer-Verlag 01.12.2011 Springer Nature B.V
Predmet:	Accuracy Artificial Intelligence Classification Computational Intelligence Control Engineering Evolutionary algorithms Focus Fuzzy sets Fuzzy systems Genetic algorithms Genetics Knapsack problem Machine learning Mathematical Logic and Foundations Mechatronics Multiple objective analysis Mutation Optimization Pareto optimization Pareto optimum Performance evaluation Robotics Sorting algorithms Systems design Visualization Multiobjective machine learning Fuzzy rule-based classification Genetics-based machine learning Genetic algorithms Evolutionary multiobjective optimization
ISSN:	1432-7643, 1433-7479
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