Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms

Peter Koro , Tome Eftimov
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Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms

Peter Koro , Tome Eftimov
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133 PAGESANGLAIS

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  • Date de publication : Jun 12, 2023
  • Langue : anglais
  • Nombre de pages : 133
  • Éditeur : Springer Nature
  • ISBN : 9783030969196
  • Dimensions : 6.1" W x 1.0" L x 9.25" H

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 KoroÅ¡ec 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.

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