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Guided Randomness in Optimization, Volume 1.

Author: Maurice Clerc
Publisher: Hoboken : Wiley, 2015.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
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  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Clerc, Maurice.
Guided Randomness in Optimization, Volume 1.
Hoboken : Wiley, ©2015
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Maurice Clerc
ISBN: 9781119136446 111913644X
OCLC Number: 913797618
Description: 1 online resource (320 pages)
Contents: 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. 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. 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. 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. 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.

Abstract:

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.

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