Archiving Strategies for Evolutionary Multi-objective Optimization Algorithms

Name: Archiving Strategies for Evolutionary Multi-objective Optimization Algorithms
Brand: Springer Nature Switzerland AG
SKU: 9783030637750
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Autor: Oliver Schütze, Carlos Hernández

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Editura: Springer Nature Switzerland AG

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This book presents an overview of archiving strategies developed over the last years by the authors...

Informații despre carte

Autor

Oliver Schütze, Carlos Hernández

Limbă

engleză

Legare

Carte - Carte broșată

Publicat

2022

Pagini

234

EAN

9783030637750

Enbook ID

38503556

Editura

Springer Nature Switzerland AG

Greutate

385

Dimensiuni

155 x 235 x 14

Descriere completă

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.

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Archiving Strategies for Evolutionary Multi-objective Optimization Algorithms

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