Archiving strategies for evolutionary multi-objective optimization algorithms [electronic resource] / Oliver Schütze, Carlos Hernández.
2021
QA76.9.A43
Formats
| Format | |
|---|---|
| BibTeX | |
| MARCXML | |
| TextMARC | |
| MARC | |
| DublinCore | |
| EndNote | |
| NLM | |
| RefWorks | |
| RIS |
Cite
Citation
Linked e-resources
Linked Resource
Concurrent users
Unlimited
Authorized users
Authorized users
Document Delivery Supplied
Can lend chapters, not whole ebooks
Details
Title
Archiving strategies for evolutionary multi-objective optimization algorithms [electronic resource] / Oliver Schütze, Carlos Hernández.
ISBN
9783030637736 (electronic bk.)
3030637735 (electronic bk.)
3030637727
9783030637729
3030637735 (electronic bk.)
3030637727
9783030637729
Published
Cham : Springer, 2021.
Language
English
Description
1 online resource (242 pages).
Item Number
10.1007/978-3-030-63773-6 doi
Call Number
QA76.9.A43
Dewey Decimal Classification
005.13
006.3
006.3
Summary
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 field of multi-objective optimization.
Bibliography, etc. Note
Includes bibliographical references.
Access Note
Access limited to authorized users.
Digital File Characteristics
text file
PDF
Source of Description
Description based on print version record.
Series
Studies in computational intelligence ; v. 938.
Available in Other Form
Linked Resources
Record Appears in
Table of Contents
Introduction
Multi-objective Optimization
The Framework
Computing the Entire Pareto Front
Computing Gap Free Pareto Fronts
Using Archivers within MOEAs
Test Problems.
Multi-objective Optimization
The Framework
Computing the Entire Pareto Front
Computing Gap Free Pareto Fronts
Using Archivers within MOEAs
Test Problems.