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Found 15 papers, 5 papers with code
DiffMAC: Diffusion Manifold Hallucination Correction for High Generalization Blind Face Restoration
Experimental results demonstrate the superiority of DiffMAC over state-of-the-art methods, with a high degree of generalization in real-world and heterogeneous settings.
Effects of Archive Size on Computation Time and Solution Quality for Multi-Objective Optimization
In this study, we examine the effects of the archive size on three aspects: (i) the quality of the selected final solution set, (ii) the total computation time for the archive maintenance and the final solution set selection, and (iii) the required memory size.
HV-Net: Hypervolume Approximation based on DeepSets
In this letter, we propose HV-Net, a new method for hypervolume approximation in evolutionary multi-objective optimization.
Learning to Approximate: Auto Direction Vector Set Generation for Hypervolume Contribution Approximation
The learned direction vector set can then be used in the $R_2^{\text{HVC}}$ indicator to improve its approximation quality.
Benchmarking Subset Selection from Large Candidate Solution Sets in Evolutionary Multi-objective Optimization
This paper aims to fill this research gap by proposing a benchmark test suite for subset selection from large candidate solution sets, and comparing some representative methods using the proposed test suite.
Clustering-Based Subset Selection in Evolutionary Multiobjective Optimization
Subset selection is an important component in evolutionary multiobjective optimization (EMO) algorithms.
Hypervolume-Optimal $μ$-Distributions on Line/Plane-based Pareto Fronts in Three Dimensions
Then, we show that a uniform solution set on the plane-based Pareto front is not always optimal for hypervolume maximization.
Fast Greedy Subset Selection from Large Candidate Solution Sets in Evolutionary Multi-objective Optimization
Especially, in an EMO algorithm with an unbounded external archive, subset selection is an essential post-processing procedure to select a pre-specified number of solutions as the final result.
Evolutionary Multi-Objective Optimization Algorithm Framework with Three Solution Sets
The three solution sets are the main population of an EMO algorithm, an external archive to store promising solutions, and a final solution set which is presented to the decision maker.
Decomposition-Based Multi-Objective Evolutionary Algorithm Design under Two Algorithm Frameworks
In the final population framework, the final population of an EMO algorithm is presented to the decision maker.
Algorithm Configurations of MOEA/D with an Unbounded External Archive
In this framework, which is referred to as the solution selection framework, the final population does not have to be a good solution set.
Lazy Greedy Hypervolume Subset Selection from Large Candidate Solution Sets
Subset selection is a popular topic in recent years and a number of subset selection methods have been proposed.
Solution Subset Selection for Final Decision Making in Evolutionary Multi-Objective Optimization
The selection of a single final solution from the obtained solutions is assumed to be done by a human decision maker.
Effects of Discretization of Decision and Objective Spaces on the Performance of Evolutionary Multiobjective Optimization Algorithms
In other studies, it is shown that the objective space discretization improves the performance on combinatorial multi-objective problems.
R2-based Hypervolume Contribution Approximation
The basic idea of the proposed method is to use different line segments only in the hypervolume contribution region for the hypervolume contribution approximation.
Optimization and Control
