Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms by Tome Eftimov

Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms by Tome Eftimov

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.

The book is well written and the presentation is easy to followIt will be useful to students and researchers dealing with metaheuristic stochastic optimization, but also to practitioners who want to know how to choose the best methods to solve the real-life problems they face. (Marcin Anholcer, zbMATH 1504.90003, 2023)

Tome Eftimov is currently a research fellow at the Jožef Stefan Institute, Ljubljana, Slovenia where he was awarded his PhD. He has since been a postdoctoral research fellow at the Dept. of Biomedical Data Science, and the Centre for Population Health Sciences, Stanford University, USA, and a research associate at the University of California, San Francisco, USA. His main areas of research include statistics, natural language processing, heuristic optimization, machine learning, and representational learning. His work related to benchmarking in computational intelligence is focused on developing more robust statistical approaches that can be used for the analysis of experimental data. 

Peter Korošec received his PhD degree from the Jožef Stefan Postgraduate School, Ljubljana, Slovenia. Since 2002 he has been a researcher at the Computer Systems Department of the Jožef Stefan Institute, Ljubljana. He has participated in the organization of various conferencesworkshops as program chair or organizer. He has successfully applied his optimization approaches to several real-world problems in engineering. Recently, he has focused on better understanding optimization algorithms so that they can be more efficiently selected and applied to real-world problems. 

The authors have presented the related tutorial at the significant related international conferences in Evolutionary Computing, including GECCO, PPSN, and SSCI.

SKU	Unavailable
ISBN 13	9783030969196
ISBN 10	3030969193
Title	Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms
Author	Tome Eftimov
Series	Natural Computing Series
Condition	Unavailable
Binding Type	Paperback
Publisher	Springer Nature Switzerland AG
Year published	2023-06-12
Number of pages	133
Cover note	Book picture is for illustrative purposes only, actual binding, cover or edition may vary.