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Titlebook: Archiving Strategies for Evolutionary Multi-objective Optimization Algorithms; Oliver Schütze,Carlos Hernández Book 2021 The Editor(s) (if

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發(fā)表于 2025-3-21 18:49:56 | 只看該作者 |倒序瀏覽 |閱讀模式
期刊全稱Archiving Strategies for Evolutionary Multi-objective Optimization Algorithms
影響因子2023Oliver Schütze,Carlos Hernández
視頻videohttp://file.papertrans.cn/162/161416/161416.mp4
發(fā)行地址Highlights recent research on Archiving Strategies for Evolutionary Multi-objective Optimization Algorithms.Provides an overview of the different archiving methods which allow convergence of Multi-obj
學科分類Studies in Computational Intelligence
圖書封面Titlebook: Archiving Strategies for Evolutionary Multi-objective Optimization Algorithms; Oliver Schütze,Carlos Hernández Book 2021 The Editor(s) (if
影響因子.This book presents an overview of archiving strategies developed over the last years by the authors that deal with suitable approximations of the sets of optimal and nearly optimal solutions of multi-objective optimization problems by means of stochastic search algorithms. All presented archivers are analyzed with respect to the approximation qualities of the limit archives that they generate and the upper bounds of the archive sizes. The convergence analysis will be done using a very broad framework that involves all existing stochastic search algorithms and that will only use minimal assumptions on the process to generate new candidate solutions. All of the presented archivers can effortlessly be coupled with any set-based multi-objective search algorithm such as multi-objective evolutionary algorithms, and the resulting hybrid method takes over the convergence properties of the chosen archiver. This book hence targets at all algorithm designers and practitioners in the fieldof multi-objective optimization..
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發(fā)表于 2025-3-21 23:26:18 | 只看該作者
Computing Gap Free Pareto Fronts,ely based on the concept of .-dominance. While the applicability of . is restricted since it stores too many points during the run of the search process, the opposite can happen for the two .-dominance based archivers. To see the latter, consider the extreme examples depicted in Fig.?.. Both sub-fig
板凳
發(fā)表于 2025-3-22 02:33:19 | 只看該作者
Computing the Set of Approximate Solutions,consequence, even the limit archives for certain archivers could contain solutions that are not optimal, but only nearly optimal. This fact, however, does not represent a problem at least from the practical point of view, as in many cases we are satisfied with such nearly optimal solutions. More pre
地板
發(fā)表于 2025-3-22 05:14:11 | 只看該作者
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發(fā)表于 2025-3-22 09:55:49 | 只看該作者
Using Archivers Within MOEAs, either as an external archive to the base algorithm, or directly as part of the selection process. In order to maintain the convergence properties of the chosen archiving strategy, it is important to assure that assumption (.) on the process to generate new solutions is satisfied.
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發(fā)表于 2025-3-22 20:02:53 | 只看該作者
Takao K. Hensch,Michela Fagiolini will hence accept all vectors . with .. So far, we have used nearly optimal (or .-approximate) solutions to obtain a finite size representation of the Pareto sets/fronts. In this chapter, we will go one step further and investigate the approximation of the entire set of approximate solutions of a g
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Archiving Strategies for Evolutionary Multi-objective Optimization Algorithms
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