EDC adopts an efficient estimation of distribution algorithm (EDA) to separately and concurrently evolve all the subpopulations in the eigenspace. It neglects the weak interactions among different subproblems, but still considers the dependencies among variables in the same subproblem.
Apr 5, 2020 · Abstract:Divide-and-conquer-based (DC-based) evolutionary algorithms (EAs) have achieved notable success in dealing with large-scale ...
Abstract: Divide-and-conquer-based (DC-based) evolutionary algorithms (EAs) have achieved notable success in dealing with large-scale optimization problems ...
Sep 6, 2024 · Divide-and-conquer-based (DC-based) evolutionary algorithms (EAs) have achieved notable success in dealing with large-scale optimization ...
A Parallel Divide-and-Conquer-Based Evolutionary Algorithm for Large-Scale Optimization · A Competitive Divide-and-Conquer Algorithm for Unconstrained Large- ...
Zhigang Ren, Yongsheng Liang, Muyi Wang, Yang Yang, An Chen: An Eigenspace Divide-and-Conquer Approach for Large-Scale Optimization.
An Eigenspace Divide-and-Conquer Approach for Large-Scale Optimization · no code implementations • 5 Apr 2020 • Zhigang Ren, Yongsheng Liang, Muyi Wang, Yang ...
This paper proposes a novel Divide-and-Conquer (DC) based EA that can not only produce high-quality solutions by solving sub-problems separately, ...
In this project, we apply divide and conquer scheme to handle big data. In the divide step, the large-scale problem is decomposed into several smaller ...
Missing: eigenspace | Show results with:eigenspace
Large-scale optimization problems that involve thousands of decision variables have extensively arisen from various industrial areas.