Archiving Strategies for Evolutionary Multi-objective Optimization Algorithms by

Description: Archiving Strategies for Evolutionary Multi-objective Optimization Algorithms by Oliver SchÜtze, Carlos Hernández 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. FORMAT Hardcover LANGUAGE English CONDITION Brand New Publisher Description 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. Back Cover 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. 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. Feature 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 Details ISBN3030637727 Series Studies in Computational Intelligence Language English Year 2021 ISBN-10 3030637727 ISBN-13 9783030637729 Format Hardcover DOI 10.1007/978-3-030-63773-6 Series Number 938 Pages 234 Publication Date 2021-01-05 Publisher Springer Nature Switzerland AG Edition 1st Imprint Springer Nature Switzerland AG Place of Publication Cham Country of Publication Switzerland UK Release Date 2021-01-05 Illustrations 44 Illustrations, color; 86 Illustrations, black and white; XIII, 234 p. 130 illus., 44 illus. in color. Author Carlos Hernández Edition Description 1st ed. 2021 Alternative 9783030637750 DEWEY 006.3 Audience Professional & Vocational We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:134084617;

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