Overview
- 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-objective evolutionary algorithms in a stochastic sense
- Presents theory as well as applications
Part of the book series: Studies in Computational Intelligence (SCI, volume 938)
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About this book
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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Table of contents (10 chapters)
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Front Matter
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Back Matter
Authors and Affiliations
Bibliographic Information
Book Title: Archiving Strategies for Evolutionary Multi-objective Optimization Algorithms
Authors: Oliver Schütze, Carlos Hernández
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-030-63773-6
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
Hardcover ISBN: 978-3-030-63772-9Published: 05 January 2021
Softcover ISBN: 978-3-030-63775-0Published: 06 January 2022
eBook ISBN: 978-3-030-63773-6Published: 04 January 2021
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: XIII, 234
Number of Illustrations: 86 b/w illustrations, 44 illustrations in colour