@article{1447475,
      author = {Eftimov, Tome, and Korošec, Peter,},
      url = {http://library.usi.edu/record/1447475},
      title = {Deep statistical comparison for meta-heuristic stochastic  optimization algorithms /},
      abstract = {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.},
      doi = {https://doi.org/10.1007/978-3-030-96917-2},
      recid = {1447475},
      pages = {1 online resource (xvii, 133 pages) :},
}