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Evolutionary many-objective optimization: A short review
Whereas evolutionary multiobjective optimization (EMO) algorithms have successfully been used in a wide range of real-world application tasks, difficulties in their scalability to many-objectiveExpand
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Performance of Decomposition-Based Many-Objective Algorithms Strongly Depends on Pareto Front Shapes
Recently, a number of high performance many-objective evolutionary algorithms with systematically generated weight vectors have been proposed in the literature. Those algorithms often showExpand
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Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning
This paper examines the interpretability-accuracy tradeoff in fuzzy rule-based classifiers using a multiobjective fuzzy genetics-based machine learning (GBML) algorithm. Our GBML algorithm is aExpand
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Modified Distance Calculation in Generational Distance and Inverted Generational Distance
In this paper, we propose the use of modified distance calculation in generational distance (GD) and inverted generational distance (IGD). These performance indicators evaluate the quality of anExpand
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Evolutionary many-objective optimization
In this paper, we first explain why many-objective problems are difficult for Pareto dominance-based evolutionary multiobjective optimization algorithms such as NSGA-II and SPEA. Then we explainExpand
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Behavior of Multiobjective Evolutionary Algorithms on Many-Objective Knapsack Problems
We examine the behavior of three classes of evolutionary multiobjective optimization (EMO) algorithms on many-objective knapsack problems. They are Pareto dominance-based, scalarizing function-based,Expand
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A Review of the Application of Multiobjective Evolutionary Fuzzy Systems: Current Status and Further Directions
Over the past few decades, fuzzy systems have been widely used in several application fields, thanks to their ability to model complex systems. The design of fuzzy systems has been successfullyExpand
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Difficulties in specifying reference points to calculate the inverted generational distance for many-objective optimization problems
Recently the inverted generational distance (IGD) measure has been frequently used for performance evaluation of evolutionary multi-objective optimization (EMO) algorithms on many-objective problems.Expand
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Evolutionary many-objective optimization by NSGA-II and MOEA/D with large populations
Evolutionary multiobjective optimization (EMO) is an active research area in the field of evolutionary computation. EMO algorithms are designed to find a non-dominated solution set that approximatesExpand
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Modification of Evolutionary Multiobjective Optimization Algorithms for Multiobjective Design of Fuzzy Rule-Based Classification Systems
We examine three methods for improving the ability of evolutionary multiobjective optimization (EMO) algorithms to find a variety of fuzzy rule-based classification systems with different tradeoffsExpand
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