9783030969165 - Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms di Eftimov, Tome; Koroec, Peter (15 risultati)

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Condizione: New. 1st ed. 2022 edition NO-PA16APR2015-KAP.

Buch. Condizione: Neu. 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 GmbH, Tiergartenstr. 17, 69121 Heidelberg 152 pp. Englisch.

Buch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - Focusing oncomprehensive comparisonsof the performance of stochastic optimization algorithms, this book provides an overview of the current approachesused to analyzealgorithm performancein a range of commonscenarios, while also addressingissues that are often overlooked.In turn, itshows how these issues can be easily avoided by applyingtheprinciplesthat have producedDeep 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, examplesfroma recently developed web-service-based e-learning tool(DSCTool) arepresented. The toolprovides users with all the functionalities needed to makerobust statistical comparison analysesinvariousstatistical scenarios.The book isintendedfornewcomers to the field and experienced researchers alike. For newcomers, it coversthe basicsofoptimization and statistical analysis,familiarizing themwith thesubject matterbefore introducingthe Deep Statistical Comparison approach. Experienced researcherscan quickly move on to the content on newstatistical approaches.The book is dividedinto 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 applicationof DeepStatistical Comparison - Chapter 8.

Hardcover. Condizione: Brand New. 150 pages. 9.25x6.10x0.38 inches. In Stock.

Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Focusing oncomprehensive comparisonsof the performance of stochastic optimization algorithms, this book provides an overview of the current approachesused to analyzealgorithm performancein a range of commonscenarios, while also addressingissues that are often overlooked.In turn, itshows how these issues can be easily avoided by applyingtheprinciplesthat have producedDeep 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, examplesfroma recently developed web-service-based e-learning tool(DSCTool) arepresented. The toolprovides users with all the functionalities needed to makerobust statistical comparison analysesinvariousstatistical scenarios.The book isintendedfornewcomers to the field and experienced researchers alike. For newcomers, it coversthe basicsofoptimization and statistical analysis,familiarizing themwith thesubject matterbefore introducingthe Deep Statistical Comparison approach. Experienced researcherscan quickly move on to the content on newstatistical approaches.The book is dividedinto 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 applicationof DeepStatistical Comparison - Chapter 8. 152 pp. Englisch.

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Buch. Condizione: Neu. Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms | Tome Eftimov (u. a.) | Buch | xvii | Englisch | 2022 | Springer | EAN 9783030969165 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.

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