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008			221015s2022 sz | s |0|| 0|eng d
020			_a9783030969172
024	7		_a10.1007/978-3-030-96917-2 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9.A43 _b2022 EB
100	1		_aEftimov, Tome _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9684997
245	1	0	_aDeep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms _cby Tome Eftimov, Peter Korošec
250			_aFirst edition 2022
264		1	_aCham _bSpringer International Publising _c2022
300			_a1 recurso en línea (XVII, 133 páginas) _b29 ilustraciones, 25 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aNatural Computing Series
505	0		_aIntroduction -- 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.
520			_aFocusing 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. 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.
988			_aSpringer_Computer_2022
650		7	_2embne _9467159 _aProgramación metaheurística
650		7	_2embne _9145705 _aOptimización matemática
650		7	_2embne _9151819 _aAlgoritmos computacionales
700	1		_aKorošec, Peter, _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9684998 _d1977-
776	0	8	_iPrinted edition: _z9783030969165
776	0	8	_iPrinted edition: _z9783030969189
776	0	8	_iPrinted edition: _z9783030969196
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-96917-2 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b10/2022 _dz _eIG _zSI