Results 11 to 20 of about 1,432,311 (298)
Random Pareto front surfaces [PDF]
CoRRThe goal of multi-objective optimisation is to identify the Pareto front surface which is the set obtained by connecting the best trade-off points. Typically this surface is computed by evaluating the objectives at different points and then interpolating
Tu, Ben +3 more
core +5 more sources
Finding Pareto-front Membership Functions in Fuzzy Data Mining [PDF]
International Journal of Computational Intelligence Systems, 2012 Transactions with quantitative values are commonly seen in real-world applications. Fuzzy mining algorithms have thus been developed recently to induce linguistic knowledge from quantitative databases.
Chun-Hao Chen +2 more
doaj +2 more sources
Linear scalarization for Pareto front identification in stochastic environments
2015 IEEE Symposium Series on Computational Intelligence, 2015 \u3cp\u3eMulti-objective multi-armed bandits (MOMAB) is a multiarm bandit variant that uses stochastic reward vectors. In this paper, we propose three MOMAB algorithms.
Madalina M. Drugan, Drugan, MM Madalina
core +3 more sources
Clustering of the approximated Pareto front
Научно-технический вестник информационных технологий, механики и оптикиIn contemporary engineering and scientific practice, multi-objective optimization often facilitates the search for compromise solutions without prescribing weight coefficients or bounds, forming a Pareto front via heuristic approximation based on genetic
A. G. Yurtaev
doaj +2 more sources
The
Statistical Analysis and Data Mining: The ASA Data Science Journal, 2021 AbstractAlgorithmic fairness seeks to identify and correct sources of bias in machine learning algorithms. Confoundingly, ensuring fairness often comes at the cost of accuracy. We provide formal tools in this work for reconciling this fundamental tension in algorithm fairness.
Susan Wei, Marc Niethammer
openaire +4 more sources
Active Learning of Pareto Fronts [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2014 This paper introduces the active learning of Pareto fronts (ALP) algorithm, a novel approach to recover the Pareto front of a multiobjective optimization problem. ALP casts the identification of the Pareto front into a supervised machine learning task. This approach enables an analytical model of the Pareto front to be built.
Campigotto, Paolo +2 more
openaire +2 more sources
On upper approximations of Pareto fronts [PDF]
Journal of Global Optimization, 2018 In one of our earlier works, we proposed to approximate Pareto fronts to multiobjective optimization problems by two-sided approximations, one from inside and another from outside of the feasible objective set, called, respectively, lower shell and upper shell. We worked there under the assumption that for a given problem an upper shell exists.
Ignacy Kaliszewski, Janusz Miroforidis
openaire +3 more sources
Preprocessing Imprecise Points for the Pareto Front [PDF]
, 2022 In the preprocessing model for uncertain data we are given a set of regions R which model the uncertainty associated with an unknown set of points P. In this model there are two phases: a preprocessing phase, in which we have access only to R, followed by a reconstruction phase, in which we have access to points in P at a certain retrieval cost C per ...
Ivor van der Hoog +3 more
openaire +4 more sources
Optimal controller comparison using Pareto fronts [PDF]
, 2010 Includes abstract.Includes bibliographical references (leaves 131-133).New design methods in Control Systems are regularly proposed. These new methods are typically compared to existing methods in a focused manner that highlights certain criteria, but ...
David Moore, Moore, David
core +2 more sources
Learning the Pareto Front with Hypernetworks
CoRR, 2020 Accepted to ICLR ...
Aviv Navon +3 more
openaire +4 more sources