Results 261 to 270 of about 14,697 (286)

Multiclass Gene Selection Using Pareto-Fronts

IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2013
Filter 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, Piyushkumar A. Mundra   +1 more
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Computing the Entire Pareto Front

2021
In 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
openaire   +1 more source

Computing Pareto fronts using distributed agents

Computers & Chemical Engineering, 2003
Abstract 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, Steinar Hauan, Arthur Westerberg   +2 more
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Visualization of a statistical approximation of the Pareto front

Applied Mathematics and Computation, 2015
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Uncertainty Quantification of Pareto Fronts

2020
Uncertainty 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, Emmanuel Pagnacco, Roberta Lima, Eduardo Souza de Cursi   +3 more
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Pareto Front Estimation Using Unit Hyperplane

2021
This 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, Keiki Takadama, Hiroyuki Sato 0003   +2 more
openaire   +1 more source

Pareto-Front Computation and Automatic Sizing of CPPLLs

8th International Symposium on Quality Electronic Design (ISQED'07), 2007
A comprehensive performance space exploration on system level offers designers a fast way to get insight into the capability of the whole system for a given technology. The authors consider a charge-pump phase-locked loop (CPPLL) system. In this paper performance space exploration is applied not only to the building blocks but to the whole CPPLL system
Jun Zou, Daniel Mueller 0001, Helmut E. Graeb, Ulf Schlichtmann   +3 more
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Pareto-Front Exploration with Uncertain Objectives

2001
We consider the problem of exploration of the set of all global optima (Pareto-points) or an approximation thereof in the context of multi-objective function optimization. Up to now, set oriented techniques assume that the evaluation of the m-dimensional vector of objectives can be done exactly which is important to steer the search process towards ...
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Niche Distributions on the Pareto Optimal Front

2003
This paper examines the use of fitness sharing in evolutionary multi-objective optimization (EMO) algorithms to form a uniform distribution of niches along the non-dominated frontier. A long-standing, implicit assumption is that fitness sharing within an equivalence class, such as the Pareto optimal set, can form dynamically stable (under selection ...
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Hybrid approach for Pareto front expansion in heuristics

Journal of the Operational Research Society, 2011
Heuristic search can be an effective multi-objective optimization tool; however, the required frequent function evaluations can exhaust computational sources. This paper explores using a hybrid approach with statistical interpolation methods to expand optimal solutions obtained by multiple criteria heuristic search.
Haluk Yapicioglu, H. Liu, Alice E. Smith, Gerry V. Dozier   +3 more
openaire   +2 more sources