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Computing the Entire Pareto Front
2021In the following three chapters we will address archivers that aim for Pareto front approximations of a given MOP. In the first step, we are interested in maintaining all non-dominated solutions that were computed during the run of an algorithm. That is, if we are given a set of candidate solutions .
Oliver Schütze, Carlos Hernández
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Layout-aware Pareto fronts of electronic circuits
2011 20th European Conference on Circuit Theory and Design (ECCTD), 2011Peer ...
Antonio Toro-Frías +3 more
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Multiclass Gene Selection Using Pareto-Fronts
IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2013Filter methods are often used for selection of genes in multiclass sample classification by using microarray data. Such techniques usually tend to bias toward a few classes that are easily distinguishable from other classes due to imbalances of strong features and sample sizes of different classes.
Jagath C. Rajapakse +1 more
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A Generator for Multiobjective Test Problems With Difficult-to-Approximate Pareto Front Boundaries
IEEE Transactions on Evolutionary Computation, 2019In some real-world applications, it has been found that the performance of multiobjective optimization evolutionary algorithms (MOEAs) may deteriorate when boundary solutions in the Pareto front (PF) are more difficult to approximate than others.
Zhenkun Wang +4 more
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Computing Pareto fronts using distributed agents
Computers & Chemical Engineering, 2003Abstract Many problems that face a business decision maker are most accurately formulated as multi-objective optimization problems. However, actually solving these problems is a difficult and computationally expensive process. In this paper, we develop and use an agent-based optimization system for efficiently generating the non-dominated solution ...
John D. Siirola +2 more
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Pareto Front Estimation Using Unit Hyperplane
2021This work proposes a method to estimate the Pareto front even in areas without objective vectors in the objective space. For the Pareto front approximation, we use a set of non-dominated points, objective vectors, in the objective space. To finely approximate the Pareto front, we need to increase the number of objective vectors. It is worth to estimate
Tomoaki Takagi +2 more
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IEEE Transactions on Cybernetics, 2019
This paper proposes a Gaussian process (GP) based co-sub-Pareto front surrogate augmentation strategy for evolutionary optimization of computationally expensive multiobjective problems.
Jian-Ping Luo +3 more
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This paper proposes a Gaussian process (GP) based co-sub-Pareto front surrogate augmentation strategy for evolutionary optimization of computationally expensive multiobjective problems.
Jian-Ping Luo +3 more
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Uncertainty Quantification of Pareto Fronts
2020Uncertainty quantification of Pareto fronts introduces new challenges connected to probabilities in infinite dimensional spaces. Indeed, Pareto fronts are, in general, manifolds belonging to infinite dimensional spaces: for instance, a curve in bi-objective optimization or a surface in three objective optimization. This article examines the methods for
Mohamed Bassi +3 more
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Visualization of a statistical approximation of the Pareto front
Applied Mathematics and Computation, 2015zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Spectral-Efficiency—Illumination Pareto Front for Energy Harvesting Enabled VLC Systems
IEEE Transactions on Communications, 2019The continuous improvement in optical energy harvesting devices motivates the development of visible light communication systems that utilize such available free energy.
A. Abdelhady +4 more
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