Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms

lt;p>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.

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.

"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)

“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)