Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms / Natural Computing Series (PDF)
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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.
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.
- Autoren: Tome Eftimov , Peter Korosec
- 2022, 1st ed. 2022, 133 Seiten, Englisch
- Verlag: Springer International Publishing
- ISBN-10: 3030969177
- ISBN-13: 9783030969172
- Erscheinungsdatum: 11.06.2022
Abhängig von Bildschirmgröße und eingestellter Schriftgröße kann die Seitenzahl auf Ihrem Lesegerät variieren.
- Dateiformat: PDF
- Größe: 3.16 MB
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