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Guided Randomness in Optimization, Volume 1.
The performance of an algorithm used depends on the GNA. This book focuses on the comparison of optimizers, it defines a stress-outcome approach which can be derived all the classic criteria (median, average, etc.) and other more sophisticated. Source-codes used for the examples are also presented,...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken :
Wiley,
2015.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Clerc, Maurice. | |
| 245 | 1 | 0 | |a Guided Randomness in Optimization, Volume 1. |
| 260 | |a Hoboken : |b Wiley, |c 2015. | ||
| 300 | |a 1 online resource (320 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a Cover; Title Page; Copyright; Contents; Preface; About this book; Organization of the book; Tools; Key points; Contact the author; Introduction; PART 1: Randomness in Optimization; 1: Necessary Risk; 1.1. No better than random search; 1.1.1. Uniform random search; 1.1.2. Sequential search; 1.1.3. Partial gradient; 1.2. Better or worse than random search; 1.2.1. Positive correlation problems; 1.2.2. Negative correlation problems; 2: Random Number Generators (RNGs); 2.1. Generator types; 2.2. True randomness; 2.3. Simulated randomness; 2.3.1. KISS; 2.3.2. Mersenne-Twister. | |
| 505 | 8 | |a 2.4. Simplified randomness2.4.1. Linear congruential generators; 2.4.2. Additive; 2.4.3. Multiplicative; 2.5. Guided randomness; 2.5.1. Gaussian; 2.5.2. Bell; 2.5.3. Cauchy; 2.5.4. Lévy; 2.5.5. Log-normal; 2.5.6. Composite distributions; 3: The Effects of Randomness; 3.1. Initialization; 3.1.1. Uniform randomness; 3.1.2. Low divergence; 3.1.3. No Man's Land techniques; 3.2. Movement; 3.3. Distribution of the Next Possible Positions (DNPP); 3.4. Confinement, constraints and repairs; 3.4.1. Strict confinement; 3.4.2. Random confinement; 3.4.3. Moderate confinement; 3.4.4. Reverse. | |
| 505 | 8 | |a 3.4.5. Reflection-diffusion3.5. Strategy selection; PART 2: Optimizer Comparison; Introduction to Part 2; 4: Algorithms and Optimizers; 4.1. The Minimaliste algorithm; 4.1.1. General description; 4.1.2. Minimaliste in practice; 4.1.3. Use of randomness; 4.2. PSO; 4.2.1. Description; 4.2.2. Use of randomness; 4.3. APS; 4.3.1. Description; 4.3.2. Uses of randomness; 4.4. Applications of randomness; 5: Performance Criteria; 5.1. Eff-Res: construction and properties; 5.1.1. Simple example using random search; 5.2. Criteria and measurements; 5.2.1. Objective criteria; 5.2.1.1. Result probabilities. | |
| 505 | 8 | |a 5.2.1.2. Effort probabilities5.2.1.3. Mean result of an effort; 5.2.1.4. Median result of an effort; 5.2.1.5. Normalized efficiency; 5.2.1.6. Mean and median cost; 5.2.2. Semi-subjective criteria; 5.2.2.1. Fragmentation of effort; 5.2.2.2. Result quality and success rate; 5.2.2.3. Global quality; 5.3. Practical construction of an Eff-Res; 5.3.1. Detailed example: (Minimaliste, Alpine 2D); 5.3.2. Qualitative interpretations; 5.4. Conclusion; 6: Comparing Optimizers; 6.1. Data collection and preprocessing; 6.2. Critical analysis of comparisons. | |
| 505 | 8 | |a 6.2.1. Influence of criteria and the number of attempts6.2.2. Influence of effort levels; 6.2.3. Global comparison; 6.2.4. Influence of the RNG; 6.3. Uncertainty in statistical analysis; 6.3.1. Independence of tests; 6.3.2. Confidence threshold; 6.3.3. Success rate; 6.4. Remarks on test sets; 6.4.1. Analysis grid; 6.4.2. Representativity; 6.5. Precision and prudence; PART 3: Appendices; 7: Mathematical Notions; 7.1. Sets closed under permutations; 7.2. Drawing with or without repetition; 7.3. Properties of the Additive and Multiplicative generators; 7.3.1. Additive; 7.3.2. Multiplicative. | |
| 520 | |a The performance of an algorithm used depends on the GNA. This book focuses on the comparison of optimizers, it defines a stress-outcome approach which can be derived all the classic criteria (median, average, etc.) and other more sophisticated. Source-codes used for the examples are also presented, this allows a reflection on the ""superfluous chance, "" succinctly explaining why and how the stochastic aspect of optimization could be avoided in some cases | ||
| 546 | |a English. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Mathematical optimization. | |
| 650 | 6 | |a Optimisation mathématique. | |
| 650 | 7 | |a Mathematical optimization |2 fast | |
| 758 | |i has work: |a Guided Randomness in Optimization, Volume 1 (Text) |1 https://id.oclc.org/worldcat/entity/E39PD3H3fjYcJTmk83cwQQwMrm |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Clerc, Maurice. |t Guided Randomness in Optimization, Volume 1. |d Hoboken : Wiley, ©2015 |z 9781848218055 |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=1998802 |z Texto completo |
| 936 | |a BATCHLOAD | ||
| 938 | |a ProQuest Ebook Central |b EBLB |n EBL1998802 | ||
| 994 | |a 92 |b IZTAP | ||