Deep Statistical Comparison for Meta-heuristic Stochastic Optimization AlgorithmsКНИГИ » НАУКА И УЧЕБА
Название: Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms Автор: Tome Eftimov, Peter Korosec Издательство: Springer Год: 2022 Страниц: 141 Размер: 10.08 МБ Формат: PDF Язык: English
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. Experienced researchers can quickly move on to the content on new statistical approaches.
Differential Evolution: From Theory to Practice Название: Differential Evolution: From Theory to Practice Автор: B. Vinoth Kumar, Diego Oliva, P. N. Suganthan Издательство: Springer Серия: Studies...
Algorithms for Optimization (2019) Название: Algorithms for Optimization Автор: Mykel J. Kochenderfer and Tim A. Wheeler Издательство: The MIT Press Год: 2019 Страниц: 521 Язык:...
Навигация
Вход на сайт
Реклама
Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms 
