Overview
- 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
Part of the book series: Natural Computing Series (NCS)
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Table of contents (8 chapters)
Keywords
About this book
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.
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Authors and Affiliations
About the authors
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.
Bibliographic Information
Book Title: Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms
Authors: Tome Eftimov, Peter Korošec
Series Title: Natural Computing Series
DOI: https://doi.org/10.1007/978-3-030-96917-2
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
Hardcover ISBN: 978-3-030-96916-5Published: 12 June 2022
Softcover ISBN: 978-3-030-96919-6Published: 12 June 2023
eBook ISBN: 978-3-030-96917-2Published: 11 June 2022
Series ISSN: 1619-7127
Series E-ISSN: 2627-6461
Edition Number: 1
Number of Pages: XVII, 133
Number of Illustrations: 4 b/w illustrations, 25 illustrations in colour
Topics: Artificial Intelligence, Probability Theory and Stochastic Processes, Statistics, general