000 | 03826nam a22005655i 4500 | ||
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001 | 978-3-030-96917-2 | ||
003 | DE-He213 | ||
005 | 20240423125433.0 | ||
007 | cr nn 008mamaa | ||
008 | 220611s2022 sz | s |||| 0|eng d | ||
020 |
_a9783030969172 _9978-3-030-96917-2 |
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024 | 7 |
_a10.1007/978-3-030-96917-2 _2doi |
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050 | 4 | _aQ334-342 | |
050 | 4 | _aTA347.A78 | |
072 | 7 |
_aUYQ _2bicssc |
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072 | 7 |
_aCOM004000 _2bisacsh |
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072 | 7 |
_aUYQ _2thema |
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082 | 0 | 4 |
_a006.3 _223 |
100 | 1 |
_aEftimov, Tome. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
|
245 | 1 | 0 |
_aDeep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms _h[electronic resource] / _cby Tome Eftimov, Peter Korošec. |
250 | _a1st ed. 2022. | ||
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2022. |
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300 |
_aXVII, 133 p. 29 illus., 25 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
||
490 | 1 |
_aNatural Computing Series, _x2627-6461 |
|
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. | ||
650 | 0 | _aArtificial intelligence. | |
650 | 0 | _aStochastic analysis. | |
650 | 0 | _aStatistics . | |
650 | 1 | 4 | _aArtificial Intelligence. |
650 | 2 | 4 | _aStochastic Analysis. |
650 | 2 | 4 | _aStatistics. |
700 | 1 |
_aKorošec, Peter. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
|
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783030969165 |
776 | 0 | 8 |
_iPrinted edition: _z9783030969189 |
776 | 0 | 8 |
_iPrinted edition: _z9783030969196 |
830 | 0 |
_aNatural Computing Series, _x2627-6461 |
|
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-030-96917-2 |
912 | _aZDB-2-SCS | ||
912 | _aZDB-2-SXCS | ||
942 | _cSPRINGER | ||
999 |
_c177922 _d177922 |