Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms

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.

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 statistical comparisons of optimization algorithms

Auteur
Tome Eftimov is currently a research fellow at the Jozef 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 KoroSec received his PhD degree from the Jozef Stefan Postgraduate School, Ljubljana, Slovenia. Since 2002 he has been a researcher at the Computer Systems Department of the Jozef Stefan Institute, Ljubljana. He has participated in the organization of various conferences workshops 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.

Texte du rabat

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.

Résumé
"The book is well written and the presentation is easy to follow. It 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)

Contenu
Introduction.- Metaheuristic Stochastic Optimization.- Benchmarking Theory.- Introduction to Statistical Analysis.- Approaches to Statistical Comparisons.- Deep Statistical Comparison in Single-Objective Optimization.- Deep Statistical Comparison in Multiobjective Optimization.- DSCTool: A Web-Service-Based E-Learning Tool.- Summary.