Deep statistical comparison for meta-heuristic stochastic optimization algorithms / Tome Eftimov, Peter Korošec.

Unlimited

Authorized users

Can lend chapters, not whole ebooks

Deep statistical comparison for meta-heuristic stochastic optimization algorithms / Tome Eftimov, Peter Korošec.

9783030969172 (electronic bk.)
3030969177 (electronic bk.)
9783030969165 (print)

Cham : Springer, 2022.

English

1 online resource (xvii, 133 pages) : illustrations (some color).

10.1007/978-3-030-96917-2 doi

QA76.9.A43

005.13

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.

Includes bibliographical references and index.

Access limited to authorized users.

Online resource; title from PDF title page (SpringerLink, viewed June 14, 2022).

Natural computing series.

Online Resources > Ebooks
All Resources