Evolutionary Intelligence: An Introduction to Theory and Applications with Matlab

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Springer Science & Business Media, 2008. 5. 15. - 584페이지

This book provides a highly accessible introduction to evolutionary computation. It details basic concepts, highlights several applications of evolutionary computation, and includes solved problems using MATLAB software and C/C++. This book also outlines some ideas on when genetic algorithms and genetic programming should be used. The most difficult part of using a genetic algorithm is how to encode the population, and the author discusses various ways to do this.

 

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Introduction to Evolutionary Computation
1
12 Brief History
2
13 Biological and Artificial Evolution
3
131 EC Terminology
4
14 Darwinian Evolution
5
141 The Premise
6
143 Slowly but Surely Process
7
145 Darwins Theory of Evolution
8
625 Conclusion
265
632 Natural Language Grammar Development
266
633 Grammar Evolution
267
Probabilistic Grammar Optimization
268
Grammar Rule Discovery
272
Unsupervised Grammar Induction
274
637 Concluding Remarks
275
64 Waveform Synthesis using Evolutionary Computation
276

15 Genetics
9
16 Evolutionary Computation
10
17 Important Paradigms in Evolutionary Computation
12
172 Genetic Programming
14
173 Evolutionary Programming
15
174 Evolution Strategies
17
18 Global Optimization
20
19 Techniques of Global Optimization
21
192 Clustering Methods
22
194 Simulated Annealing
26
195 Statistical Global Optimization Algorithms
27
197 Multi Objective Optimization
28
Summary
30
Principles of Evolutionary Algorithms
31
22 Structure of Evolutionary Algorithms
32
221 Illustration
34
23 Components of Evolutionary Algorithms
36
25 EvaluationFitness Function
37
27 Selection
38
271 Rank Based Fitness Assignment
39
272 Multiobjective Ranking
41
273 Roulette Wheel selection
43
274 Stochastic universal sampling
44
275 Local Selection
45
276 Truncation Selection
47
277 Comparison of Selection Properties
49
278 MATLAB Code Snippet for Selection
51
28 Recombination
52
282 Real Valued Recombination
53
283 Binary Valued Recombination Crossover
57
29 Mutation
61
291 Real Valued Mutation
62
292 Binary mutation
63
293 Real Valued Mutation with Adaptation of Step Sizes
64
294 Advanced Mutation
65
295 Other Types of Mutation Flip Bit
66
296 MATLAB Code Snippet for Mutation
67
2102 Local Reinsertion
68
211 Reproduction Operator
69
2111 MATLAB Code Snippet for Reproduction
70
213 Advantages of Evolutionary Algorithms
72
214 Multiobjective Evolutionary Algorithms
73
215 Critical Issues in Designing an Evolutionary Algorithm
74
Summary
75
Genetic Algorithms with Matlab
77
32 History of Genetic Algorithm
80
33 Genetic Algorithm Definition
81
34 Models of Evolution
82
35 Operational Functionality of Genetic Algorithms
83
36 Genetic Algorithms An Example
84
37 Genetic Representation
86
382 Concatenated MultiParameter Mapped FixedPoint Coding
87
39 Schema Theorem and Theoretical Background
88
391 Building Block Hypothesis
89
392 Working of Genetic Algorithms
90
393 Sets and Subsets
91
394 The Dynamics of a Schema
92
395 Compensating for Destructive Effects
93
396 Mathematical Models
94
397 Illustrations based on Schema Theorem
97
Genotype and Fitness
100
3101 NonConventional Genotypes
102
311 Advanced Operators in GA
104
3114 Mutation and Naive Evolution
105
3117 Diploidy and Dominance
106
313 Comparison of GA with Other Methods
107
3134 Iterated Search
108
314 Types of Genetic Algorithm
109
3142 Parallel GA
110
3143 Hybrid GA
112
3144 Adaptive GA
113
3145 Integrated Adaptive GA IAGA
116
3146 Messy GA
117
3147 Generational GA GGA
120
3148 Steady State GA SSGA
121
315 Advantages of GA
122
316 Matlab Examples of Genetic Algorithms
123
3162 Illustration 1 Maximizing the given Function
126
3163 Illustration 2 Optimization of a Multidimensional NonConvex Function
132
3164 Illustration 3 Traveling Salesman Problem
136
3165 Illustration 4 GA using Float Representation
143
3166 Illustration 5 Constrained Problem
158
3167 Illustration 6 Maximum of any given Function
161
Summary
167
Review Questions
169
Genetic Programming Concepts
171
42 A Brief History of Genetic Programming
175
43 The Lisp Programming Language
176
44 Operations of Genetic Programming
177
442 Creating a Random Population
178
443 Fitness Test
179
446 Selection Functions
180
447 Crossover Operation
182
448 Mutation
183
45 An Illustration
185
46 The GP Paradigm in Machine Learning
186
47 Preparatory Steps of Genetic Programming
188
472 The Function Set
189
475 The Termination Criterion
190
48 Flow Chart of Genetic Programming
191
49 Type Constraints in Genetic Programming
193
410 Enhanced Versions of Genetic Programming
195
4101 Metagenetic Programming
196
4102 Cartesian Genetic Programming
201
4103 Strongly Typed Genetic Programming STGP
209
411 Advantages of using Genetic Programming
217
Review Questions
218
Parallel Genetic Algorithms
219
An Overview
220
53 Classification of PGA
223
54 Parallel Population Models for Genetic Algorithms
224
541 Classification of Global Population Models
225
542 Global Population Models
226
544 Local Population Models
228
55 Models Based on Distribution of Population
230
552 Distributed PGA
231
56 PGA Models Based on Implementation
232
562 Island PGA
234
563 Cellular PGA
236
57 PGA Models Based on Parallelism
238
58 Communication Topologies
240
59 Hierarchical Parallel Algorithms
241
510 Object Orientation OO and Parallelization
243
511 Recent Advancements
244
512 Advantages of Parallel Genetic Algorithms
246
Summary
247
Applications of Evolutionary Algorithms
249
612 Fingerprint Characteristics
250
613 Fingerprint Recognition using EA
255
614 Experimental Results
257
615 Conclusion and Future Work
258
An Evolutionary Programming Algorithm using SelfOrganized Criticality
260
623 Case Studies
261
624 Results of Numerical Experiments
263
643 Conclusion and Results
279
65 Scheduling Earth Observing Satellites with Evolutionary Algorithms
282
652 EOS Scheduling by Evolutionary Algorithms and other Optimization Techniques
284
653 Results
286
654 Future Work
288
66 An Evolutionary Computation Approach to Scenariobased Riskreturn Portfolio Optimization for General Risk Measures
289
663 Evolutionary Portfolio Optimization
291
664 Numerical Results Implementation
292
665 Results
293
666 Conclusion
296
Applications of Genetic Algorithms
297
711 Research Background
298
712 Proposed Approach and Case Studies
303
713 Discussion of Results
305
714 Concluding Remarks
308
721 Active Network Synthesis Using GAs
309
722 Example of an AutomaticallySynthesized Network
311
723 Limitations of Automatic Network Synthesis
313
73 A Genetic Algorithm for Mixed Macro and Standard Cell Placement
314
732 Experimental Results
318
74 Knowledge Acquisition on Image Procssing Based on Genetic Algorithms
319
741 Methods
320
742 Results and Discussions Performance of Segmentation
325
743 Concluding Remarks
327
751 Genetic Clustering in Image Segmentation
328
752 KMeans Clustering Model
329
754 Results and Conclusions
330
76 Genetic AlgorithmBased Performance Analysis of SelfExcited Induction Generator
331
761 Modelling of SEIG System
332
762 Genetic Algorithm Optimization
334
763 Results and Discussion
335
764 Concluding Remarks
337
77 Feature Selection for Anns Using Genetic Algorithms in Condition Monitoring
338
771 Signal Acquisition
340
773 Genetic Algorithms
341
775 Results ANN
342
776 Concluding Remarks
343
781 Overview of Parallel STAP
344
782 Genetic Algorithm Methodology
345
783 Numerical Results
347
784 Concluding Remarks
348
79 A MultiObjective Genetic Algorithm for onChip RealTime Adaptation of a MultiCarrier Based Telecommunications Receiver
349
791 MCCDMA Receiver
350
793 Results
353
794 Concluding Remarks
355
7101 Realization of the Neural Network
357
7102 Implementation of the Genetic Training Algorithm
362
7103 Experimental Results
364
7104 Concluding Remarks
366
Genetic Programming Applications
367
811 Robocode Rules
368
812 Evolving Robocode Strategies using Genetic Programming
369
813 Results
374
814 Concluding Remarks
375
821 Method and Results
376
822 Discussion
377
831 Genetic Programming Initial Tree Generation
378
832 Combining Genetic Programming with High Energy Physics Data
380
833 Selecting Genetic Programming Parameters
385
834 Testing Genetic Programming on D+ K+π+π
389
835 Concluding Remarks
394
84 Using Genetic Programming to Generate Protocol Adaptors for Interprocess Communication
395
841 Prerequisites of Interprocess Communication
397
843 Evolving Protocols
400
844 The Experiment
403
845 Concluding Remarks
405
An Application of Genetic Programming
406
851 Background
407
852 FGP for Predication in DJIA Index
408
853 Concluding Remarks
411
86 Genetic Programming within Civil Engineering
412
862 Applications of Genetic Programming in Civil Engineering
413
865 An Example of Structural Optimization
414
866 10 Member Planar Truss
415
868 Model
416
869 ViewVisualisation
417
8610 Concluding Remarks
419
87 Chemical Process Controller Design using Genetic Programming
420
872 ARX Process Description
423
874 GP Problem Formulation
425
875 GP Configuration and Implementation Aspects
426
876 Results
428
877 Concluding Remarks
430
88 Trading Applications of Genetic Programming
431
Forecasting or Prediction
433
Finding Causal Relationships
434
884 Concluding Remarks
435
892 Model
438
893 Problems to be Solved
441
895 Concluding Remarks
443
Applications of Parallel Genetic Algorithm
445
912 Applying Genetic Algorithms to Timetabling
446
913 A Parallel Algorithm
450
914 Results
452
915 Conclusion
453
922 The DNA Fragment Assembly Problem
454
923 DNA Sequencing Process
455
924 DNA Fragment Assembly Using the Sequential GA
457
925 Implementation Details
458
926 DNA Fragment Assembly Problem using the Parallel GA
460
927 Experimental Results
462
93 Investigating Parallel Genetic Algorithms on Job Shop Scheduling Problems
469
932 Job Shop Scheduling Problem
470
933 Genetic Representation and Specific Operators
471
934 Parallel Genetic Algorithms for JSSP
473
935 Computational Results
475
936 Comparison of PGA Models
477
94 Parallel Genetic Algorithm for Graph Coloring Problem
479
942 Genetic Operators for GCP
480
943 Experimental Verification
484
944 Conclusion
486
952 Ordering Problems
487
953 Traveling Salesman Problem
488
954 Distributed Genetic Algorithm
491
Appendix A Glossary
503
Appendix B Abbreviations
517
Appendix C Research Projects
521
C3 Dynamic Prediction of Web Requests
522
C6 Imperfect Evolutionary Systems
523
C8 Classification with Ant Colony Optimization
524
C10 Coarsegrained Dynamics for Generalized Recombination
525
C12 An Investigation on Noisy Environments in Evolutionary Multiobjective Optimization
526
C13 Interactive Evolutionary Computationbased Hearing Aid Fitting
527
C15 Knowledge Interaction with Genetic Programming in Mechatronic Systems Design Using Bond Graphs
528
C18 Accelerating Evolutionary Algorithms with Gaussian Process Fitness Function Models
529
C20 An Evolutionary Algorithm for Solving Nonlinear Bilevel Programming Based on a New Constrainthandling Scheme
530
C22 Genetic Recurrent Fuzzy System by Coevolutionary Computation with DivideandConquer Technique
531
C24 A Comparative Study of Three Evolutionary Algorithms Incorporating Different Amounts of Domain Knowledge for Node Covering Problem
532
Appendix D MATLAB Toolboxes
533
Commercial Software Packages
537
Ga Source Codes in C Language
547
EC ClassCode Libraries and Software Kits
559
Bibliography
569
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