Papers Multiobjective Optimization
“Multiobjective Optimization” 태그가 달린 논문 155편 · 필터 해제
The Cone epsilon-Dominance: An Approach for Evolutionary Multiobjective Optimization
We propose the cone epsilon-dominance approach to improve convergence and diversity in multiobjective evolutionary algorithms (MOEAs). A cone-eps-MOEA is presented and compared with MOEAs based on the standard Pareto rel…
DiversityEvolutionary AlgorithmsMultiobjective OptimizationTchebycheff Procedure for Multi-task Text Classification
Multi-task Learning methods have achieved great progress in text classification. However, existing methods assume that multi-task text classification problems are convex multiobjective optimization problems, which is unr…
ClassificationGeneral ClassificationMultiobjective OptimizationMulti-Task Learning+2Efficient Continuous Pareto Exploration in Multi-Task Learning
Tasks in multi-task learning often correlate, conflict, or even compete with each other. As a result, a single solution that is optimal for all tasks rarely exists. Recent papers introduced the concept of Pareto optimali…
BIG-bench Machine LearningMultiobjective OptimizationMulti-Task LearningEffects of Discretization of Decision and Objective Spaces on the Performance of Evolutionary Multiobjective Optimization Algorithms
Recently, the discretization of decision and objective spaces has been discussed in the literature. In some studies, it is shown that the decision space discretization improves the performance of evolutionary multi-objec…
Multiobjective OptimizationRegularisation Can Mitigate Poisoning Attacks: A Novel Analysis Based on Multiobjective Bilevel Optimisation
Machine Learning (ML) algorithms are vulnerable to poisoning attacks, where a fraction of the training data is manipulated to deliberately degrade the algorithms' performance. Optimal poisoning attacks, which can be form…
Bilevel OptimizationData PoisoningL2 RegularizationMultiobjective OptimizationAn Assignment Problem Formulation for Dominance Move Indicator
Dominance move (DoM) is a binary quality indicator to compare solution sets in multiobjective optimization. The indicator allows a more natural and intuitive relation when comparing solution sets. It is Pareto compliant …
Multiobjective OptimizationvalidHow to Evaluate Solutions in Pareto-based Search-Based Software Engineering? A Critical Review and Methodological Guidance
With modern requirements, there is an increasing tendency of considering multiple objectives/criteria simultaneously in many Software Engineering (SE) scenarios. Such a multi-objective optimization scenario comes with an…
Multiobjective OptimizationMODRL/D-AM: Multiobjective Deep Reinforcement Learning Algorithm Using Decomposition and Attention Model for Multiobjective Optimization
Recently, a deep reinforcement learning method is proposed to solve multiobjective optimization problem. In this method, the multiobjective optimization problem is decomposed to a number of single-objective optimization …
Deep Reinforcement LearningMultiobjective Optimizationreinforcement-learningReinforcement Learning+1Best of Both Worlds: AutoML Codesign of a CNN and its Hardware Accelerator
Neural architecture search (NAS) has been very successful at outperforming human-designed convolutional neural networks (CNN) in accuracy, and when hardware information is present, latency as well. However, NAS-designed …
AutoMLGeneral ClassificationGPUimage-classification+4Dominance Move calculation using a MIP approach for comparison of multi and many-objective optimization solution sets
Dominance move (DoM) is a binary quality indicator that can be used in multiobjective optimization. It can compare solution sets while representing some important features such as convergence, spread, uniformity, and car…
Multiobjective OptimizationExpert Learning through Generalized Inverse Multiobjective Optimization: Models, Insights and Algorithms
We study a new unsupervised learning task of inferring objective functions or constraints of a multiobjective decision making model, based on a set of observed decisions. Specifically, we formulate such a learning proble…
ClusteringDecision MakingMultiobjective OptimizationPareto Multi-Task Learning
Multi-task learning is a powerful method for solving multiple correlated tasks simultaneously. However, it is often impossible to find one single solution to optimize all the tasks, since different tasks might conflict w…
Multiobjective OptimizationMulti-Task LearningDeep Innovation Protection: Confronting the Credit Assignment Problem in Training Heterogeneous Neural Architectures
Deep reinforcement learning approaches have shown impressive results in a variety of different domains, however, more complex heterogeneous architectures such as world models require the different neural components to be…
Deep Reinforcement LearningMultiobjective OptimizationMax-value Entropy Search for Multi-Objective Bayesian Optimization
We consider the problem of multi-objective (MO) blackbox optimization using expensive function evaluations, where the goal is to approximate the true Pareto-set of solutions by minimizing the number of function evaluatio…
Bayesian OptimisationBayesian OptimizationMultiobjective OptimizationDeep Innovation Protection
Evolutionary-based optimization approaches have recently shown promising results in domains such as Atari and robot locomotion but less so in solving 3D tasks directly from pixels. This paper presents a method called Dee…
Multiobjective OptimizationPareto-optimal data compression for binary classification tasks
The goal of lossy data compression is to reduce the storage cost of a data set $X$ while retaining as much information as possible about something ($Y$) that you care about. For example, what aspects of an image $X$ cont…
Binary ClassificationClassificationClusteringData Compression+3Pareto Optimal Demand Response Based on Energy Costs and Load Factor in Smart Grid
Demand response for residential users is essential to the realization of modern smart grids. This paper proposes a multiobjective approach to designing a demand response program that considers the energy costs of residen…
Multiobjective OptimizationALWANN: Automatic Layer-Wise Approximation of Deep Neural Network Accelerators without Retraining
The state-of-the-art approaches employ approximate computing to reduce the energy consumption of DNN hardware. Approximate DNNs then require extensive retraining afterwards to recover from the accuracy loss caused by the…
Multiobjective OptimizationScalarizing Functions in Bayesian Multiobjective Optimization
Scalarizing functions have been widely used to convert a multiobjective optimization problem into a single objective optimization problem. However, their use in solving (computationally) expensive multi- and many-objecti…
Multiobjective OptimizationTuning metaheuristics by sequential optimization of regression models
Tuning parameters is an important step for the application of metaheuristics to problem classes of interest. In this work we present a tuning framework based on the sequential optimization of perturbed regression models.…
Multiobjective Optimizationregression