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E-raamat: Guided Randomness in Optimization, Volume 1

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  • Ilmumisaeg: 04-May-2015
  • Kirjastus: ISTE Ltd and John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781119136453
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Guided Randomness in Optimization, Volume 1Suurem pilt
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 04-May-2015
  • Kirjastus: ISTE Ltd and John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781119136453
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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.

Preface xi
Introduction xv
Part 1 Randomness In Optimization 1(48)
Chapter 1 Necessary Risk
3(10)
1.1 No better than random search
3(4)
1.1.1 Uniform random search
4(1)
1.1.2 Sequential search
5(1)
1.1.3 Partial gradient
5(2)
1.2 Better or worse than random search
7(6)
1.2.1 Positive correlation problems
8(2)
1.2.2 Negative correlation problems
10(3)
Chapter 2 Random Number Generators (RNGs)
13(20)
2.1 Generator types
14(1)
2.2 True randomness
15(1)
2.3 Simulated randomness
15(2)
2.3.1 KISS
16(1)
2.3.2 Mersenne-Twister
16(1)
2.4 Simplified randomness
17(7)
2.4.1 Linear congruential generators
18(2)
2.4.2 Additive
20(2)
2.4.3 Multiplicative
22(2)
2.5 Guided randomness
24(9)
2.5.1 Gaussian
24(1)
2.5.2 Bell
24(3)
2.5.3 Cauchy
27(1)
2.5.4 Levy
28(1)
2.5.5 Log-normal
28(1)
2.5.6 Composite distributions
28(5)
Chapter 3 The Effects Of Randomness
33(16)
3.1 Initialization
34(3)
3.1.1 Uniform randomness
34(2)
3.1.2 Low divergence
36(1)
3.1.3 No Man's Land techniques
37(1)
3.2 Movement
37(3)
3.3 Distribution of the Next Possible Positions (DNPP)
40(2)
3.4 Confinement, constraints and repairs
42(4)
3.4.1 Strict confinement
44(1)
3.4.2 Random confinement
44(1)
3.4.3 Moderate confinement
45(1)
3.4.4 Reverse
45(1)
3.4.5 Reflection-diffusion
45(1)
3.5 Strategy selection
46(3)
Part 2 Optimizer Comparison 49(82)
Chapter 4 Algorithms And Optimizers
53(16)
4.1 The Minimaliste algorithm
54(5)
4.1.1 General description
54(1)
4.1.2 Minimaliste in practice
54(3)
4.1.3 Use of randomness
57(2)
4.2 PSO
59(3)
4.2.1 Description
59(1)
4.2.2 Use of randomness
60(2)
4.3 APS
62(4)
4.3.1 Description
62(3)
4.3.2 Uses of randomness
65(1)
4.4 Applications of randomness
66(3)
Chapter 5 Performance Criteria
69(40)
5.1 Eff-Res: construction and properties
69(5)
5.1.1 Simple example using random search
71(3)
5.2 Criteria and measurements
74(20)
5.2.1 Objective criteria
77(10)
5.2.2 Semi-subjective criteria
87(7)
5.3 Practical construction of an Eff-Res
94(14)
5.3.1 Detailed example: (Minimaliste, Alpine 2D)
95(11)
5.3.2 Qualitative interpretations
106(2)
5.4 Conclusion
108(1)
Chapter 6 Comparing Optimizers
109(22)
6.1 Data collection and preprocessing
111(3)
6.2 Critical analysis of comparisons
114(9)
6.2.1 Influence of criteria and the number of attempts
115(1)
6.2.2 Influence of effort levels
115(2)
6.2.3 Global comparison
117(4)
6.2.4 Influence of the RNG
121(2)
6.3 Uncertainty in statistical analysis
123(2)
6.3.1 Independence of tests
125(1)
6.3.2 Confidence threshold
125(1)
6.3.3 Success rate
125(1)
6.4 Remarks on test sets
125(5)
6.4.1 Analysis grid
126(3)
6.4.2 Representativity
129(1)
6.5 Precision and prudence
130(1)
Part 3 Appendices 131(154)
Chapter 7 Mathematical Notions
133(6)
7.1 Sets closed under permutations
133(1)
7.2 Drawing with or without repetition
133(2)
7.3 Properties of the Additive and Multiplicative generators
135(4)
7.3.1 Additive
136(1)
7.3.2 Multiplicative
136(3)
Chapter 8 Biases And Signatures
139(8)
8.1 The impossible plateau
139(1)
8.2 Optimizer signatures
140(7)
Chapter 9 A Pseudo-Scientific Article
147(8)
9.1 Article
147(4)
9.2 Criticism
151(4)
Chapter 10 Common Mistakes
155(4)
Chapter 11 Unnecessary Randomness? List-Based Optimizers
159(8)
11.1 Truncated lists
160(2)
11.2 Semi-empirical lists
162(1)
11.3 Micro-robots
163(4)
Chapter 12 Problems
167(6)
12.1 Deceptive 1 (Flash)
167(1)
12.2 Deceptive 2 (Comb)
167(1)
12.3 Deceptive 3 (Brush)
168(1)
12.4 Alpine
168(1)
12.5 Rosenbrock
168(1)
12.6 Pressure vessel
169(1)
12.7 Sphere
169(1)
12.8 Traveling salesman: six cities
170(1)
12.9 Traveling salesman: fourteen cities (Burma 14)
170(1)
12.10 Tripod
171(1)
12.11 Gear train
171(2)
Chapter 13 Source Codes
173(112)
13.1 Random generation and sampling
173(18)
13.1.1 Preamble for Scilab codes
174(1)
13.1.2 Drawing of a pseudo-random number, according to options
174(4)
13.1.3 True randomness
178(1)
13.1.4 Guided randomness
179(4)
13.1.5 Uniform initializations (continuous, combinatorial)
183(1)
13.1.6 Regular initializations (Sobol, Halton)
183(1)
13.1.7 No Man's Land techniques
184(2)
13.1.8 Sampling
186(3)
13.1.9 Movements and confinements
189(2)
13.2 Useful tools
191(1)
13.3 Combinatorial operations
191(7)
13.4 Random algorithm
198(2)
13.5 Minimaliste algorithm
200(5)
13.6 SPSO algorithm
205(11)
13.7 APS algorithm
216(18)
13.8 APSO algorithm
234(7)
13.9 Problems
241(14)
13.9.1 Problem definitions
241(13)
13.9.2 Problem landscape
254(1)
13.10 Treatment of results
255(8)
13.10.1 Quality (including curves)
255(1)
13.10.2 Other criteria (including curves)
256(5)
13.10.3 Construction of an Eff-Res
261(2)
13.11 Treatment of the Eff-Res
263(8)
13.11.1 Graphic representation
263(1)
13.11.2 Interpolation
264(1)
13.11.3 Performance criteria (including curves)
265(6)
13.12 Histograms, polar diagrams
271(2)
13.13 Other figures
273(4)
13.14 Tests (bias, correlation)
277(8)
Bibliography 285(8)
Index 293
Maurice Clerc is recognized as one of the foremost PSO specialists in the world. A former France Telecom Research and Development engineer, he maintains his research activities as a consultant for the XPS (eXtended Particle Swarm) project.
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