| 书目名称 | Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms |
| 编辑 | Tome Eftimov,Peter Korošec |
| 视频video | http://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 |
| 丛书名称 | Natural Computing Series |
| 图书封面 |  |
| 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 |
| 关键词 | Metaheuristics; Stochastic Optimization; Optimization; Benchmarking; Statistical Analysis; Multiobjective |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-030-96917-2 |
| isbn_softcover | 978-3-030-96919-6 |
| isbn_ebook | 978-3-030-96917-2Series ISSN 1619-7127 Series E-ISSN 2627-6461 |
| issn_series | 1619-7127 |
| copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
|Archiver|手机版|小黑屋|
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