Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms

Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms (Natural Computing Series)
Author: Tome Eftimov and Peter Korošec
Publisher Finelybook 出版社:Springer; 1st ed. 2022 edition (June 12, 2022)
Language 语言:English
pages 页数:150 pages
ISBN-10 书号:3030969169
ISBN-13 书号:9783030969165

Book Description
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 Author: 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.

下载地址 Download隐藏内容需1积分,VIP免费,请先 !没有帐号? 注 册 一个!
觉得文章有用就打赏一下
未经允许不得转载:finelybook » Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms

评论 抢沙发

  • 昵称 (必填)
  • 邮箱 (必填)
  • 网址

觉得文章有用就打赏一下

非常感谢你的打赏,我们将继续给力更多优质内容,让我们一起创建更加美好的网络世界!

支付宝扫一扫打赏

微信扫一扫打赏