Algorithm Portfolios: Advances, Applications, and Challenges

This book covers algorithm portfolios, multi-method schemes that harness optimization algorithms into a joint framework to solve optimization problems. It is expected to be a primary reference point for researchers and doctoral students in relevant domains that seek a quick exposure to the field. The presentation focuses primarily on the applicability of the methods and the non-expert reader will find this book useful for starting designing and implementing algorithm portfolios. The book familiarizes the reader with algorithm portfolios through current advances, applications, and open problems. Fundamental issues in building effective and efficient algorithm portfolios such as selection of constituent algorithms, allocation of computational resources, interaction between algorithms and parallelism vs. sequential implementations are discussed. Several new applications are analyzed and insights on the underlying algorithmic designs are provided. Future directions, new challenges, andopen problems in the design of algorithm portfolios and applications are explored to further motivate research in this field.

1138538016
Algorithm Portfolios: Advances, Applications, and Challenges

This book covers algorithm portfolios, multi-method schemes that harness optimization algorithms into a joint framework to solve optimization problems. It is expected to be a primary reference point for researchers and doctoral students in relevant domains that seek a quick exposure to the field. The presentation focuses primarily on the applicability of the methods and the non-expert reader will find this book useful for starting designing and implementing algorithm portfolios. The book familiarizes the reader with algorithm portfolios through current advances, applications, and open problems. Fundamental issues in building effective and efficient algorithm portfolios such as selection of constituent algorithms, allocation of computational resources, interaction between algorithms and parallelism vs. sequential implementations are discussed. Several new applications are analyzed and insights on the underlying algorithmic designs are provided. Future directions, new challenges, andopen problems in the design of algorithm portfolios and applications are explored to further motivate research in this field.

69.99 In Stock
Algorithm Portfolios: Advances, Applications, and Challenges

Algorithm Portfolios: Advances, Applications, and Challenges

Algorithm Portfolios: Advances, Applications, and Challenges

Algorithm Portfolios: Advances, Applications, and Challenges

eBook1st ed. 2021 (1st ed. 2021)

$69.99 

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Overview

This book covers algorithm portfolios, multi-method schemes that harness optimization algorithms into a joint framework to solve optimization problems. It is expected to be a primary reference point for researchers and doctoral students in relevant domains that seek a quick exposure to the field. The presentation focuses primarily on the applicability of the methods and the non-expert reader will find this book useful for starting designing and implementing algorithm portfolios. The book familiarizes the reader with algorithm portfolios through current advances, applications, and open problems. Fundamental issues in building effective and efficient algorithm portfolios such as selection of constituent algorithms, allocation of computational resources, interaction between algorithms and parallelism vs. sequential implementations are discussed. Several new applications are analyzed and insights on the underlying algorithmic designs are provided. Future directions, new challenges, andopen problems in the design of algorithm portfolios and applications are explored to further motivate research in this field.


Product Details

ISBN-13: 9783030685140
Publisher: Springer-Verlag New York, LLC
Publication date: 03/24/2021
Series: SpringerBriefs in Optimization
Sold by: Barnes & Noble
Format: eBook
File size: 3 MB

Table of Contents

1. Metaheuristic optimization algorithms.- 2. Algorithm portfolios.- 3. Selection of constituent algorithms.- 4. Allocation of computation resources.- 5. Sequential and parallel models.- 6. Recent applications.- 7. Epilogue.- References.
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