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.
General
| Imprint: |
Springer Nature Switzerland AG
|
| Country of origin: |
Switzerland |
| Series: |
Natural Computing Series |
| Release date: |
June 2023 |
| First published: |
2022 |
| Authors: |
Tome Eftimov
• Peter KoroÅ¡ec
|
| Dimensions: |
235 x 155mm (L x W) |
| Pages: |
133 |
| Edition: |
1st ed. 2022 |
| ISBN-13: |
978-3-03-096919-6 |
| Categories: |
Books
Promotions
|
| LSN: |
3-03-096919-3 |
| Barcode: |
9783030969196 |
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