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Titlebook: Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms; Tome Eftimov,Peter Koro?ec Book 2022 The Editor(s) (if

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書目名稱Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms
編輯Tome Eftimov,Peter Koro?ec
視頻videohttp://file.papertrans.cn/265/264674/264674.mp4
概述Presents a comprehensive comparison of the performance of stochastic optimization algorithms.Includes an introduction to benchmarking and statistical analysis.Provides a web-based tool for making stat
叢書名稱Natural Computing Series
圖書封面Titlebook: Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms;  Tome Eftimov,Peter Koro?ec Book 2022 The Editor(s) (if
描述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: Int
出版日期Book 2022
關鍵詞Metaheuristics; Stochastic Optimization; Optimization; Benchmarking; Statistical Analysis; Multiobjective
版次1
doihttps://doi.org/10.1007/978-3-030-96917-2
isbn_softcover978-3-030-96919-6
isbn_ebook978-3-030-96917-2Series ISSN 1619-7127 Series E-ISSN 2627-6461
issn_series 1619-7127
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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,Deep Statistical Comparison in?Single-Objective Optimization,e data is also important in practice. Finally, an extended version of the Deep Statistical Comparison ranking scheme for handling high-dimensional data is introduced as well as its application for investigating the exploration and exploitation capabilities of the compared algorithms.
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https://doi.org/10.1007/978-3-031-06916-1e data is also important in practice. Finally, an extended version of the Deep Statistical Comparison ranking scheme for handling high-dimensional data is introduced as well as its application for investigating the exploration and exploitation capabilities of the compared algorithms.
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