Papers Multiobjective Optimization
“Multiobjective Optimization” 태그가 달린 논문 155편 · 필터 해제
A multiobjective continuation method to compute the regularization path of deep neural networks
Sparsity is a highly desired feature in deep neural networks (DNNs) since it ensures numerical efficiency, improves the interpretability of models (due to the smaller number of relevant features), and robustness. For lin…
Multiobjective OptimizationAchieving Diversity in Objective Space for Sample-efficient Search of Multiobjective Optimization Problems
Efficiently solving multi-objective optimization problems for simulation optimization of important scientific and engineering applications such as materials design is becoming an increasingly important research topic. Th…
DiversityMultiobjective OptimizationOptimizing fairness tradeoffs in machine learning with multiobjective meta-models
Improving the fairness of machine learning models is a nuanced task that requires decision makers to reason about multiple, conflicting criteria. The majority of fair machine learning methods transform the error-fairness…
FairnessMultiobjective OptimizationA framework for fully autonomous design of materials via multiobjective optimization and active learning: challenges and next steps
In order to deploy machine learning in a real-world self-driving laboratory where data acquisition is costly and there are multiple competing design criteria, systems need to be able to intelligently sample while balanci…
Active LearningMultiobjective OptimizationImproving Performance Insensitivity of Large-scale Multiobjective Optimization via Monte Carlo Tree Search
The large-scale multiobjective optimization problem (LSMOP) is characterized by simultaneously optimizing multiple conflicting objectives and involving hundreds of decision variables. Many real-world applications in engi…
Multiobjective OptimizationSensitivityEfficiently Tackling Million-Dimensional Multiobjective Problems: A Direction Sampling and Fine-Tuning Approach
We define very large-scale multiobjective optimization problems as optimizing multiple objectives (VLSMOPs) with more than 100,000 decision variables. These problems hold substantial significance, given the ubiquity of r…
Multiobjective OptimizationRecommendation SystemsThompson SamplingCharacterization of Constrained Continuous Multiobjective Optimization Problems: A Performance Space Perspective
Constrained multiobjective optimization has gained much interest in the past few years. However, constrained multiobjective optimization problems (CMOPs) are still unsatisfactorily understood. Consequently, the choice of…
BenchmarkingMultiobjective OptimizationATM-R: An Adaptive Tradeoff Model with Reference Points for Constrained Multiobjective Evolutionary Optimization
The goal of constrained multiobjective evolutionary optimization is to obtain a set of well-converged and welldistributed feasible solutions. To complete this goal, there should be a tradeoff among feasibility, diversity…
DiversityEvolutionary AlgorithmsMultiobjective OptimizationThe Hybridization of Branch and Bound with Metaheuristics for Nonconvex Multiobjective Optimization
A hybrid framework combining the branch and bound method with multiobjective evolutionary algorithms is proposed for nonconvex multiobjective optimization. The hybridization exploits the complementary character of the tw…
Evolutionary AlgorithmsMultiobjective OptimizationThe Hypervolume Indicator Hessian Matrix: Analytical Expression, Computational Time Complexity, and Sparsity
The problem of approximating the Pareto front of a multiobjective optimization problem can be reformulated as the problem of finding a set that maximizes the hypervolume indicator. This paper establishes the analytical e…
Multiobjective OptimizationSecond-order methodsA Robust Scientific Machine Learning for Optimization: A Novel Robustness Theorem
Scientific machine learning (SciML) is a field of increasing interest in several different application fields. In an optimization context, SciML-based tools have enabled the development of more efficient optimization met…
Multiobjective OptimizationNeural Optimization Machine: A Neural Network Approach for Optimization
A novel neural network (NN) approach is proposed for constrained optimization. The proposed method uses a specially designed NN architecture and training/optimization procedure called Neural Optimization Machine (NOM). T…
Multiobjective OptimizationNeural Architecture Search as Multiobjective Optimization Benchmarks: Problem Formulation and Performance Assessment
The ongoing advancements in network architecture design have led to remarkable achievements in deep learning across various challenging computer vision tasks. Meanwhile, the development of neural architecture search (NAS…
Multiobjective OptimizationNeural Architecture SearchEvolutionary Multiparty Distance Minimization
In the field of evolutionary multiobjective optimization, the decision maker (DM) concerns conflicting objectives. In the real-world applications, there usually exist more than one DM and each DM concerns parts of these …
Multiobjective OptimizationTowards Fairness-Aware Multi-Objective Optimization
Recent years have seen the rapid development of fairness-aware machine learning in mitigating unfairness or discrimination in decision-making in a wide range of applications. However, much less attention has been paid to…
BIG-bench Machine LearningDecision MakingFairnessMultiobjective OptimizationBalancing the trade-off between cost and reliability for wireless sensor networks: a multi-objective optimized deployment method
The deployment of the sensor nodes (SNs) always plays a decisive role in the system performance of wireless sensor networks (WSNs). In this work, we propose an optimal deployment method for practical heterogeneous WSNs w…
Multiobjective OptimizationBi-fidelity Evolutionary Multiobjective Search for Adversarially Robust Deep Neural Architectures
Deep neural networks have been found vulnerable to adversarial attacks, thus raising potentially concerns in security-sensitive contexts. To address this problem, recent research has investigated the adversarial robustne…
Adversarial RobustnessMultiobjective OptimizationNeural Architecture SearchTowards KAB2S: Learning Key Knowledge from Single-Objective Problems to Multi-Objective Problem
As "a new frontier in evolutionary computation research", evolutionary transfer optimization(ETO) will overcome the traditional paradigm of zero reuse of related experience and knowledge from solved past problems in rese…
Multiobjective OptimizationSchedulingA Survey on Learnable Evolutionary Algorithms for Scalable Multiobjective Optimization
Recent decades have witnessed great advancements in multiobjective evolutionary algorithms (MOEAs) for multiobjective optimization problems (MOPs). However, these progressively improved MOEAs have not necessarily been eq…
Evolutionary AlgorithmsMultiobjective OptimizationSurveyDigital Twin Data Modelling by Randomized Orthogonal Decomposition and Deep Learning
A digital twin is a surrogate model that has the main feature to mirror the original process behavior. Associating the dynamical process with a digital twin model of reduced complexity has the significant advantage to ma…
Computational EfficiencyCPUMultiobjective Optimization