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Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms (Hardcover)
Published by Springer Nature Switzerland AG, Cham, 2022
ISBN 10: 3030969169 ISBN 13: 9783030969165
Language: English
Hardcover. Condition: 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.
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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
£ 124.13
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Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms (Paperback)
Published by Springer Nature Switzerland AG, Cham, 2023
ISBN 10: 3030969193 ISBN 13: 9783030969196
Language: English
Paperback. Condition: 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.
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Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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£ 124.12
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Condition: As New. Unread book in perfect condition.
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Condition: As New. Unread book in perfect condition.
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Condition: New. 1st ed. 2022 edition NO-PA16APR2015-KAP.
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Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms
Published by Springer, Berlin|Springer International Publishing|Springer, 2023
ISBN 10: 3030969193 ISBN 13: 9783030969196
Language: English
£ 114.42
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Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms
Published by Springer, Berlin|Springer International Publishing|Springer, 2022
ISBN 10: 3030969169 ISBN 13: 9783030969165
Language: English
£ 114.42
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Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms
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Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms
Published by Springer International Publishing, 2023
ISBN 10: 3030969193 ISBN 13: 9783030969196
Language: English
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Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms
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£ 185.73
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Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms
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Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms (Paperback)
Published by Springer Nature Switzerland AG, Cham, 2023
ISBN 10: 3030969193 ISBN 13: 9783030969196
Language: English
£ 226.63
Convert currency£ 27.52 shipping from Australia to U.S.A.Quantity: 1 available
Add to basketPaperback. Condition: 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.
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Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms (Hardcover)
Published by Springer Nature Switzerland AG, Cham, 2022
ISBN 10: 3030969169 ISBN 13: 9783030969165
Language: English
£ 229.67
Convert currency£ 27.52 shipping from Australia to U.S.A.Quantity: 1 available
Add to basketHardcover. Condition: 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.
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£ 161.12
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