Editore: Springer International Publishing, 2023
ISBN 10: 3030969193 ISBN 13: 9783030969196
Lingua: Inglese
Da: Buchpark, Trebbin, Germania
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Editore: Springer, Berlin|Springer International Publishing|Springer, 2023
ISBN 10: 3030969193 ISBN 13: 9783030969196
Lingua: Inglese
Da: moluna, Greven, Germania
EUR 127,40
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Editore: Springer, Berlin|Springer International Publishing|Springer, 2022
ISBN 10: 3030969169 ISBN 13: 9783030969165
Lingua: Inglese
Da: moluna, Greven, Germania
EUR 127,40
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Da: Ria Christie Collections, Uxbridge, Regno Unito
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Da: GreatBookPrices, Columbia, MD, U.S.A.
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 147,22
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Da: Best Price, Torrance, CA, U.S.A.
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Da: Books Puddle, New York, NY, U.S.A.
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Da: California Books, Miami, FL, U.S.A.
EUR 182,08
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Da: GreatBookPrices, Columbia, MD, U.S.A.
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Da: Books Puddle, New York, NY, U.S.A.
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Editore: Springer Nature Switzerland AG, Cham, 2022
ISBN 10: 3030969169 ISBN 13: 9783030969165
Lingua: Inglese
Da: Grand Eagle Retail, Mason, OH, U.S.A.
EUR 147,69
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Aggiungi al carrelloHardcover. Condizione: new. Hardcover. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Da: Lucky's Textbooks, Dallas, TX, U.S.A.
EUR 147,92
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Da: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
EUR 206,80
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Da: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
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Da: Revaluation Books, Exeter, Regno Unito
EUR 215,61
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Da: Revaluation Books, Exeter, Regno Unito
EUR 217,52
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Aggiungi al carrelloHardcover. Condizione: Brand New. 150 pages. 9.25x6.10x0.38 inches. In Stock.
Editore: Springer Nature Switzerland AG, Cham, 2023
ISBN 10: 3030969193 ISBN 13: 9783030969196
Lingua: Inglese
Da: Grand Eagle Retail, Mason, OH, U.S.A.
EUR 164,77
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Aggiungi al carrelloPaperback. Condizione: new. Paperback. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Da: Kennys Bookstore, Olney, MD, U.S.A.
EUR 257,20
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Da: Kennys Bookstore, Olney, MD, U.S.A.
EUR 258,65
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Editore: Springer Nature Switzerland AG, Cham, 2023
ISBN 10: 3030969193 ISBN 13: 9783030969196
Lingua: Inglese
Da: AussieBookSeller, Truganina, VIC, Australia
EUR 260,96
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Aggiungi al carrelloPaperback. Condizione: new. Paperback. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Editore: Springer Nature Switzerland AG, Cham, 2022
ISBN 10: 3030969169 ISBN 13: 9783030969165
Lingua: Inglese
Da: AussieBookSeller, Truganina, VIC, Australia
EUR 264,46
Convertire valutaQuantità: 1 disponibili
Aggiungi al carrelloHardcover. Condizione: new. Hardcover. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Da: Majestic Books, Hounslow, Regno Unito
EUR 185,02
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Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 188,83
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Da: Majestic Books, Hounslow, Regno Unito
EUR 197,92
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Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 202,02
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