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Deep Statistical Comparison Meta Heuristic by Eftimov Tome (21 results)
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Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms (Natural Computing Series)
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
£ 126.63
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Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms (Natural Computing Series)
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
£ 126.63
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Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketCondition: New. In.
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Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms
Language: English
Published by Springer, Berlin|Springer International Publishing|Springer, 2023
ISBN 10: 3030969193 ISBN 13: 9783030969196
Condition: New.
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Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms
Language: English
Published by Springer, Berlin|Springer International Publishing|Springer, 2022
ISBN 10: 3030969169 ISBN 13: 9783030969165
Gebunden. Condition: New.
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Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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£ 141.18
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Condition: New. 1st ed. 2022 edition NO-PA16APR2015-KAP.
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Taschenbuch. Condition: Neu. Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms | Tome Eftimov (u. a.) | Taschenbuch | Natural Computing Series | xvii | Englisch | 2023 | Springer | EAN 9783030969196 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Paperback. Condition: Brand New. 150 pages. 9.25x6.10x0.33 inches. In Stock.
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Hardcover. Condition: Brand New. 150 pages. 9.25x6.10x0.38 inches. In Stock.
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Condition: new. Questo è un articolo print on demand.
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Condition: new. Questo è un articolo print on demand.
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Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms
Language: English
Published by Springer, Springer Jun 2023, 2023
ISBN 10: 3030969193 ISBN 13: 9783030969196
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Focusing on comprehensive comparisons of the performance of stochastic optimization algorithms, this book provides an overview of the current approaches used to analyze algorithm performance in a range of common scenarios, while also addressing issues that are often overlooked. In turn, it shows how these issues can be easily avoided by applying the principles that have produced Deep Statistical Comparison and its variants. The focus is on statistical analyses performed using single-objective and multi-objective optimization data. At the end of the book, examples from a recently developed web-service-based e-learning tool (DSCTool) are presented. The tool provides users with all the functionalities needed to make robust statistical comparison analyses in various statistical scenarios.The book is intended for newcomers to the field and experienced researchers alike. For newcomers, it covers the basics of optimization and statistical analysis, familiarizing them with the subject matter before introducing the Deep Statistical Comparison approach. Experienced researchers can quickly move on to the content on new statistical approaches. The book is divided into three parts:Part I: Introduction to optimization, benchmarking, and statistical analysis - Chapters 2-4.Part II: Deep Statistical Comparison of meta-heuristic stochastic optimization algorithms - Chapters 5-7.Part III: Implementation and application of Deep Statistical Comparison - Chapter 8.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 152 pp. Englisch.
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Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms
Language: English
Published by Springer, Springer Jun 2022, 2022
ISBN 10: 3030969169 ISBN 13: 9783030969165
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Buch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Focusing on comprehensive comparisons of the performance of stochastic optimization algorithms, this book provides an overview of the current approaches used to analyze algorithm performance in a range of common scenarios, while also addressing issues that are often overlooked. In turn, it shows how these issues can be easily avoided by applying the principles that have produced Deep Statistical Comparison and its variants. The focus is on statistical analyses performed using single-objective and multi-objective optimization data. At the end of the book, examples from a recently developed web-service-based e-learning tool (DSCTool) are presented. The tool provides users with all the functionalities needed to make robust statistical comparison analyses in various statistical scenarios.The book is intended for newcomers to the field and experienced researchers alike. For newcomers, it covers the basics of optimization and statistical analysis, familiarizing them with the subject matter before introducing the Deep Statistical Comparison approach. Experienced researchers can quickly move on to the content on new statistical approaches. The book is divided into three parts:Part I: Introduction to optimization, benchmarking, and statistical analysis - Chapters 2-4.Part II: Deep Statistical Comparison of meta-heuristic stochastic optimization algorithms - Chapters 5-7.Part III: Implementation and application of Deep Statistical Comparison - Chapter 8.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 152 pp. Englisch.
