Sumathi / Hamsapriya / Surekha Evolutionary Intelligence

An Introduction to Theory and Applications with Matlab
1. Auflage 2008
ISBN: 978-3-540-75382-7
Verlag: Springer
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 600 Seiten

"This book gives a good introduction to evolutionary computation for those who are first entering the field and are looking for insight into the underlying mechanisms behind them. Emphasizing the scientific and machine learning applications of genetic algorithms instead of applications to optimization and engineering, the book could serve well in an actual course on adaptive algorithms. The authors include excellent problem sets, these being divided up into ""thought exercises"" and ""computer exercises"" in genetic algorithm. Practical use of genetic algorithms demands an understanding of how to implement them, and the authors do so in the last two chapters of the book by giving the applications in various fields. This book also outlines some ideas on when genetic algorithms and genetic programming should be used, and this is useful since a newcomer to the field may be tempted to view a genetic algorithm as merely a fancy Monte Carlo simulation. The most difficult part of using a genetic algorithm is how to encode the population, and the authors discuss various ways to do this. Various ""exotic"" approaches to improve the performance of genetic algorithms are also discussed such as the ""messy"" genetic algorithms, adaptive genetic algorithm and hybrid genetic algorithm."

Autoren/Hrsg.

Sumathi, S.

Hamsapriya, T.

Surekha, P.

Inhaltsverzeichnis

1;Preface;5
1.1;About the Book;6
1.2;Salient Features;6
1.3;Organization of the Book;6
1.4;About the Authors;7
1.5;Acknowledgement;9
2;Contents;10
3;Introduction to Evolutionary Computation;21
3.1;1.1 Introduction;21
3.2;1.2 Brief History;22
3.3;1.3 Biological and Artificial Evolution;23
3.4;1.3.1 EC Terminology;24
3.5;1.3.2 Natural Evolution – The Inspiration from Biology;24
3.6;1.4 Darwinian Evolution;25
3.7;1.4.1 The Premise;26
3.8;1.4.2 Natural Selection;26
3.9;1.4.3 Slowly but Surely Process;27
3.10;1.4.4 A Theory in Crisis;27
3.11;1.4.5 Darwin’s Theory of Evolution;28
3.12;1.5 Genetics;29
3.13;1.5.1 The Molecular Basis for Inheritance;29
3.14;1.6 Evolutionary Computation;30
3.15;1.7 Important Paradigms in Evolutionary Computation;32
3.16;1.7.1 Genetic Algorithms;32
3.17;1.7.2 Genetic Programming;34
3.18;1.7.3 Evolutionary Programming;35
3.19;1.7.4 Evolution Strategies;37
3.20;1.8 Global Optimization;40
3.21;1.9 Techniques of Global Optimization;41
3.22;1.9.1 Branch and Bound;41
3.23;1.9.2 Clustering Methods;42
3.24;1.9.3 Hybrid Methods;42
3.25;1.9.4 Simulated Annealing;46
3.26;1.9.5 Statistical Global Optimization Algorithms;47
3.27;1.9.6 Tabu Search;47
3.28;1.9.7 Multi Objective Optimization;48
3.29;Summary;50
3.30;Review Questions;50
4;Principles of Evolutionary Algorithms;51
4.1;2.1 Introduction;51
4.2;2.2 Structure of Evolutionary Algorithms;52
4.3;2.2.1 Illustration;54
4.4;2.3 Components of Evolutionary Algorithms;56
4.5;2.4 Representation;56
4.6;2.5 Evaluation/Fitness Function;57
4.7;2.6 Population Initialization;57
4.8;2.7 Selection;58
4.9;2.7.1 Rank Based Fitness Assignment;59
4.10;2.7.2 Multi–objective Ranking;61
4.11;2.7.3 Roulette Wheel selection;63
4.12;2.7.4 Stochastic universal sampling;64
4.13;2.7.5 Local Selection;65
4.14;2.7.6 Truncation Selection;67
4.15;2.7.7 Comparison of Selection Properties;69
4.16;2.7.8 MATLAB Code Snippet for Selection;71
4.17;2.8 Recombination;72
4.18;2.8.1 Discrete Recombination;72
4.19;2.8.2 Real Valued Recombination;73
4.20;2.8.3 Binary Valued Recombination (Crossover);77
4.21;2.9 Mutation;81
4.22;2.9.1 Real Valued Mutation;82
4.23;2.9.2 Binary mutation;83
4.24;2.9.3 Real Valued Mutation with Adaptation of Step Sizes;84
4.25;2.9.4 Advanced Mutation;85
4.26;2.9.5 Other Types of Mutation;86
4.27;2.9.6 MATLAB Code Snippet for Mutation;87
4.28;2.10 Reinsertion;87
4.29;2.10.1 Global Reinsertion;87
4.30;2.10.2 Local Reinsertion;88
4.31;2.11 Reproduction Operator;89
4.32;2.11.1 MATLAB Code Snippet for Reproduction;90
4.33;2.12 Categorization of Parallel Evolutionary Algorithms;90
4.34;2.13 Advantages of Evolutionary Algorithms;92
4.35;2.14 Multi-objective Evolutionary Algorithms;93
4.36;2.15 Critical Issues in Designing an Evolutionary Algorithm;94
4.37;Summary;95
4.38;Review Questions;95
5;Genetic Algorithms with Matlab;96
5.1;3.1 Introduction;96
5.2;3.2 History of Genetic Algorithm;99
5.3;3.3 Genetic Algorithm Definition;100
5.4;3.4 Models of Evolution;101
5.5;3.5 Operational Functionality of Genetic Algorithms;102
5.6;3.6 Genetic Algorithms – An Example;103
5.7;3.7 Genetic Representation;105
5.8;3.8 Genetic Algorithm Parameters;105
5.9;3.8.1 Multi-Parameters;105
5.10;3.8.2 Concatenated, Multi-Parameter, Mapped, Fixed- Point Coding;106
5.11;3.8.3 Exploitable Techniques;106
5.12;3.9 Schema Theorem and Theoretical Background;107
5.13;3.9.1 Building Block Hypothesis;108
5.14;3.9.2 Working of Genetic Algorithms;109
5.15;3.9.3 Sets and Subsets;110
5.16;3.9.4 The Dynamics of a Schema;111
5.17;3.9.5 Compensating for Destructive Effects;112
5.18;3.9.6 Mathematical Models;113
5.19;3.9.7 Illustrations based on Schema Theorem;116
5.20;3.10 Solving a Problem: Genotype and Fitness;119
5.21;3.10.1 Non-Conventional Genotypes;121
5.22;3.11 Advanced Operators in GA;123
5.23;3.11.1 Inversion and Reordering;123
5.24;3.11.2 Epistasis;123
5.25;3.11.3 Deception;123
5.26;3.11.4 Mutation and Naive Evolution;124
5.27;3.11.5 Niche and Speciation;124
5.28;3.11.6 Restricted Mating;124
5.29;3.11.7 Diploidy and Dominance;125
5.30;3.12 Important Issues in the Implementation of a GA;125
5.31;3.13 Comparison of GA with Other Methods;126
5.32;3.13.1 Neural Nets;126
5.33;3.13.2 Random Search;126
5.34;3.13.3 Gradient Methods;126
5.35;3.13.4 Iterated Search;127
5.36;3.13.5 Simulated Annealing;127
5.37;3.14 Types of Genetic Algorithm;128
5.38;3.14.1 Sequential GA;128
5.39;3.14.2 Parallel GA;129
5.40;3.14.3 Hybrid GA;131
5.41;3.14.4 Adaptive GA;132
5.42;3.14.5 Integrated Adaptive GA (IAGA);135
5.43;3.14.6 Messy GA;136
5.44;3.14.7 Generational GA (GGA);139
5.45;3.14.8 Steady State GA (SSGA);140
5.46;3.15 Advantages of GA;141
5.47;3.16 Matlab Examples of Genetic Algorithms 3.16.1 Genetic Algorithm Operations Implemented in MATLAB;142
5.48;Reproduction;142
5.49;Selection;142
5.50;Crossover;143
5.51;Fitness Function;144
5.52;Mutation;144
5.53;3.16.2 Illustration 1 – Maximizing the given Function;145
5.54;3.16.3 Illustration 2 – Optimization of a Multidimensional Non- Convex Function;151
5.55;3.16.4 Illustration 3 – Traveling Salesman Problem;155
5.56;3.16.5 Illustration 4 – GA using Float Representation;162
5.57;3.16.6 Illustration 5 – Constrained Problem;177
5.58;3.16.7 Illustration 6 – Maximum of any given Function;180
5.59;Summary;186
5.60;Review Questions;188
6;Genetic Programming Concepts;189
6.1;4.1 Introduction;189
6.2;4.2 A Brief History of Genetic Programming;193
6.3;4.3 The Lisp Programming Language;194
6.4;4.4 Operations of Genetic Programming;195
6.5;4.4.1 Creating an Individual;195
6.6;4.4.2 Creating a Random Population;196
6.7;4.4.3 Fitness Test;197
6.8;4.4.4 Functions and Terminals;197
6.9;4.4.5 The Genetic Operations;197
6.10;4.4.6 Selection Functions;198
6.11;4.4.7 Crossover Operation;200
6.12;4.4.8 Mutation;201
6.13;4.4.9 User Decisions;201
6.14;4.5 An Illustration;203
6.15;4.6 The GP Paradigm in Machine Learning;204
6.16;4.7 Preparatory Steps of Genetic Programming;206
6.17;4.7.1 The Terminal Set;206
6.18;4.7.2 The Function Set;207
6.19;4.7.3 The Fitness Function;207
6.20;4.7.4 The Algorithm Control Parameters;207
6.21;4.7.5 The Termination Criterion;208
6.22;–;208
6.23;4.8 Flow – Chart of Genetic Programming;209
6.24;4.9 Type Constraints in Genetic Programming;211
6.25;4.10 Enhanced Versions of Genetic Programming;213
6.26;4.10.1 Meta-genetic Programming;214
6.27;4.10.2 Cartesian Genetic Programming;219
6.28;4.10.3 Strongly Typed Genetic Programming (STGP);227
6.29;4.11 Advantages of using Genetic Programming;235
6.30;Summary;235
6.31;Review Questions;236
7;Parallel Genetic Algorithms;237
7.1;5.1 Introduction;237
7.2;5.2 Parallel and Distributed Computer Architectures: An Overview;238
7.3;5.3 Classification of PGA;241
7.4;5.4 Parallel Population Models for Genetic Algorithms;242
7.5;5.4.1 Classification of Global Population Models;243
7.6;5.4.2 Global Population Models;244
7.7;5.4.3 Regional Population Models;244
7.8;5.4.4 Local Population Models;246
7.9;5.5 Models Based on Distribution of Population;248
7.10;5.5.1 Centralized PGA;248
7.11;5.5.2 Distributed PGA;249
7.12;5.6 PGA Models Based on Implementation;250
7.13;5.6.1 Master–slave/Farming PGA;250
7.14;5.6.2 Island PGA;252
7.15;5.6.3 Cellular PGA;254
7.16;5.7 PGA Models Based on Parallelism;256
7.17;5.7.1 Global with Migration (coarse-grained);256
7.18;5.7.2 Global with Migration (fine-grained);256
7.19;5.8 Communication Topologies;258
7.20;5.9 Hierarchical Parallel Algorithms;259
7.21;5.10 Object Orientation (OO) and Parallelization;261
7.22;5.11 Recent Advancements;262
7.23;5.12 Advantages of Parallel Genetic Algorithms;264
7.24;Summary;265
7.25;Review Questions;265
8;Applications of Evolutionary Algorithms;267
8.1;6.1 A Fingerprint Recognizer using Fuzzy Evolutionary Programming 6.1.1 Introduction;267
8.2;6.1.2 Fingerprint Characteristics;268
8.3;6.1.3 Fingerprint Recognition using EA;273
8.4;6.1.4 Experimental Results;275
8.5;6.1.5 Conclusion and Future Work;276
8.6;6.2 An Evolutionary Programming Algorithm for Automatic Engineering Design 6.2.1 Introduction;276
8.7;6.2.2 EPSOC: An Evolutionary Programming Algorithm using Self- Organized Criticality;278
8.8;6.2.3 Case Studies;279
8.9;6.2.4 Results of Numerical Experiments;281
8.10;6.2.5 Conclusion;283
8.11;6.3 Evolutionary Computing as a Tool for Grammar Development 6.3.1 Introduction;283
8.12;6.3.2 Natural Language Grammar Development;284
8.13;6.3.3 Grammar Evolution;285
8.14;6.3.4 GRAEL-1: Probabilistic Grammar Optimization;286
8.15;6.3.5 GRAEL-2: Grammar Rule Discovery;290
8.16;6.3.6 GRAEL-3: Unsupervised Grammar Induction;292
8.17;6.3.7 Concluding Remarks;293
8.18;6.4 Waveform Synthesis using Evolutionary Computation 6.4.1 Introduction;294
8.19;6.4.2 Evolutionary Manipulation of Waveforms;294
8.20;Crossover, Mutation and Fitness Evaluation;295
8.21;6.4.3 Conclusion and Results;297
8.22;Appendix: Mathematical Model;298
8.23;6.5 Scheduling Earth Observing Satellites with Evolutionary Algorithms 6.5.1 Introduction;300
8.24;6.5.2 EOS Scheduling by Evolutionary Algorithms and other Optimization Techniques;302
8.25;6.5.3 Results;304
8.26;6.5.4 Future Work;306
8.27;6.6 An Evolutionary Computation Approach to Scenario-based Risk- return Portfolio Optimization for General Risk Measures 6.6.1 Introduction;307
8.28;6.6.2 Portfolio Optimization;307
8.29;6.6.3 Evolutionary Portfolio Optimization;309
8.30;6.6.4 Numerical Results;310
8.31;6.6.5 Results;311
8.32;6.6.6 Conclusion;314
9;Applications of Genetic Algorithms;315
9.1;7.1 Assembly and Disassembly Planning by Using Fuzzy Logic & Genetic Algorithms;315
9.2;7.1.1 Research Background;316
9.3;7.1.2 Proposed Approach and Case Studies;321
9.4;7.1.3 Discussion of Results;323
9.5;7.1.4 Concluding Remarks;326
9.6;7.2 Automatic Synthesis of Active Electronic Networks Using Genetic Algorithms;326
9.7;7.2.1 Active Network Synthesis Using GAs;327
9.8;7.2.2 Example of an Automatically-Synthesized Network;329
9.9;7.2.3 Limitations of Automatic Network Synthesis;331
9.10;7.2.4 Concluding Remarks;331
9.11;7.3 A Genetic Algorithm for Mixed Macro and Standard Cell Placement;332
9.12;7.3.1 Genetic Algorithm for Placement;332
9.13;7.3.2 Experimental Results;336
9.14;7.4 Knowledge Acquisition on Image Procssing Based on Genetic Algorithms;337
9.15;7.4.1 Methods;338
9.16;7.4.2 Results and Discussions;343
9.17;7.4.3 Concluding Remarks;345
9.18;7.5 Map Segmentation by Colour Cube Genetic K-Mean Clustering;345
9.19;7.5.1 Genetic Clustering in Image Segmentation;346
9.20;7.5.2 K-Means Clustering Model;347
9.21;7.5.3 Genetic Implementation;347
9.22;7.5.4 Results and Conclusions;348
9.23;7.6 Genetic Algorithm-Based Performance Analysis of Self- Excited Induction Generator;349
9.24;7.6.1 Modelling of SEIG System;350
9.25;7.6.2 Genetic Algorithm Optimization;352
9.26;7.6.3 Results and Discussion;353
9.27;7.6.4 Concluding Remarks;355
9.28;7.7 Feature Selection for Anns Using Genetic Algorithms in Condition Monitoring;356
9.29;7.7.1 Signal Acquisition;358
9.30;7.7.2 Neural Networks;358
9.31;7.7.3 Genetic Algorithms;359
9.32;7.7.4 Training and Simulation;359
9.33;7.7.5 Results;360
9.34;7.7.6 Concluding Remarks;361
9.35;7.8 A Genetic Algorithm Approach to Scheduling Communications for a Class of Parallel Space-Time Adaptive Processing Algorithms;361
9.36;7.8.1 Overview of Parallel STAP;362
9.37;7.8.2 Genetic Algorithm Methodology;363
9.38;7.8.3 Numerical Results;365
9.39;7.8.4 Concluding Remarks;366
9.40;7.9 A Multi-Objective Genetic Algorithm for on-Chip Real-Time Adaptation of a Multi- Carrier Based Telecommunications Receiver;367
9.41;7.9.1 MC-CDMA Receiver;368
9.42;7.9.2 Multi-objective Genetic Algorithm (GA);368
9.43;7.9.3 Results;371
9.44;7.9.4 Concluding Remarks;373
9.45;7.10 A VLSI Implementation of an Analog Neural Network Suited for Genetic Algorithms;373
9.46;7.10.1 Realization of the Neural Network;375
9.47;7.10.2 Implementation of the Genetic Training Algorithm;380
9.48;7.10.3 Experimental Results;382
9.49;7.10.4 Concluding Remarks;384
10;Genetic Programming Applications;385
10.1;8.1 GP-Robocode: Using Genetic Programming to Evolve Robocode Players;385
10.2;8.1.1 Robocode Rules;386
10.3;8.1.2 Evolving Robocode Strategies using Genetic Programming;387
10.4;8.1.3 Results;392
10.5;8.1.4 Concluding Remarks;393
10.6;8.2 Prediction of Biochemical Reactions using Genetic Programming;393
10.7;8.2.1 Method and Results;394
10.8;8.2.2 Discussion;395
10.9;8.3 Application of Genetic Programming to High Energy Physics Event Selection;395
10.10;8.3.1 Genetic Programming;396
10.11;8.3.2 Combining Genetic Programming with High Energy Physics Data;398
10.12;8.3.3 Selecting Genetic Programming Parameters;403
10.13;8.3.4 Testing Genetic Programming on;407
10.14;8.3.5 Concluding Remarks;412
10.15;8.4 Using Genetic Programming to Generate Protocol Adaptors for Interprocess Communication;413
10.16;8.4.1 Prerequisites of Interprocess Communication;415
10.17;8.4.2 Specifying Protocols;415
10.18;8.4.3 Evolving Protocols;418
10.19;8.4.4 The Experiment;421
10.20;8.4.5 Concluding Remarks;423
10.21;8.5 Improving Technical Analysis Predictions: An Application of Genetic Programming;424
10.22;8.5.1 Background;425
10.23;8.5.2 FGP for Predication in DJIA Index;426
10.24;8.5.3 Concluding Remarks;429
10.25;8.6 Genetic Programming within Civil Engineering;430
10.26;8.6.1 Generational Genetic Programming;430
10.27;8.6.2 Applications of Genetic Programming in Civil Engineering;431
10.28;8.6.3 Application of Genetic Programming in Structural Engineering;431
10.29;8.6.4 Structural Encoding;431
10.30;8.6.5 An Example of Structural Optimization;432
10.31;8.6.6 10 Member Planar Truss;433
10.32;8.6.7 Controller-GP Tableau;433
10.33;8.6.8 Model;434
10.34;8.6.9 View-Visualisation;435
10.35;8.6.10 Concluding Remarks;437
10.36;8.7 Chemical Process Controller Design using Genetic Programming;438
10.37;8.7.1 Dynamic Reference Control Problem;438
10.38;8.7.2 ARX Process Description;441
10.39;8.7.3 CSTR (Continuous Stirred Tank Reactor) Process Description;441
10.40;8.7.4 GP Problem Formulation;443
10.41;8.7.5 GP Configuration and Implementation Aspects;444
10.42;8.7.6 Results;446
10.43;8.7.7 Concluding Remarks;448
10.44;8.8 Trading Applications of Genetic Programming;449
10.45;8.8.1 Application: Forecasting or Prediction;451
10.46;8.8.2 Application: Finding Causal Relationships;452
10.47;8.8.3 Application: Building Trading Rules;452
10.48;8.8.4 Concluding Remarks;453
10.49;8.9 Artificial Neural Network Development by Means of Genetic Programming with Graph Codification;453
10.50;8.9.1 State of the Art;453
10.51;8.9.2 Model;456
10.52;8.9.3 Problems to be Solved;459
10.53;8.9.4 Results and Comparison with Other Methods;459
10.54;8.9.5 Concluding Remarks;461
11;Applications of Parallel Genetic Algorithm;462
11.1;9.1 Timetabling Problem 9.1.1 Introduction;462
11.2;9.1.2 Applying Genetic Algorithms to Timetabling;463
11.3;9.1.3 A Parallel Algorithm;467
11.4;9.1.4 Results;469
11.5;9.1.5 Conclusion;470
11.6;9.2 Assembling DNA Fragments with a Distributed Genetic Algorithm 9.2.1 Introduction;470
11.7;9.2.2 The DNA Fragment Assembly Problem;471
11.8;9.2.3 DNA Sequencing Process;472
11.9;9.2.4 DNA Fragment Assembly Using the Sequential GA;474
11.10;9.2.5 Implementation Details;475
11.11;9.2.6 DNA Fragment Assembly Problem using the Parallel GA;477
11.12;9.2.7 Experimental Results;479
11.13;Conclusions;485
11.14;9.3 Investigating Parallel Genetic Algorithms on Job Shop Scheduling Problems 9.3.1 Introduction;486
11.15;9.3.2 Job Shop Scheduling Problem;487
11.16;9.3.3 Genetic Representation and Specific Operators;488
11.17;9.3.4 Parallel Genetic Algorithms for JSSP;490
11.18;9.3.5 Computational Results;492
11.19;The Effect of Parallelizing GAs;492
11.20;9.3.6 Comparison of PGA Models;494
11.21;9.4 Parallel Genetic Algorithm for Graph Coloring Problem 9.4.1 Introduction;496
11.22;Migration Model of Parallel Genetic Algorithm;496
11.23;9.4.2 Genetic Operators for GCP;497
11.24;Sum-product Partition Crossover;497
11.25;9.4.3 Experimental Verification;501
11.26;9.4.4 Conclusion;503
11.27;9.5 Robust and Distributed Genetic Algorithm for Ordering Problems 9.5.1 Introduction;503
11.28;9.5.2 Ordering Problems;504
11.29;9.5.3 Traveling Salesman Problem;505
11.30;9.5.4 Distributed Genetic Algorithm;508
11.31;Results for Hamiltonian Cycle TSP;516
11.32;Results for Oliver’s Hamiltonian Cycle TSP;517
11.33;Conclusion;519
12;Appendix – A Glossary;520
12.1;A;520
12.2;B;520
12.3;C;521
12.4;D;522
12.5;E;523
12.6;F;525
12.7;G;525
12.8;H;527
12.9;I;527
12.10;L;527
12.11;M;528
12.12;N;528
12.13;O;529
12.14;P;530
12.15;R;530
12.16;S;531
12.17;T;532
12.18;V;533
13;Appendix – B Abbreviations;534
14;Appendix – C Research Projects;537
14.1;C.1 Evolutionary Simulation-based Validation;537
14.2;C.2 Automatic Generation of Validation Stimuli for Application- specific Processors;537
14.3;C.3 Dynamic Prediction ofWeb Requests;538
14.4;C.4 Analog Genetic Encoding for the Evolution of Circuits and Networks;538
14.5;C.5 An Evolutionary Algorithm for Global Optimization Based on Level- set Evolution and Latin Squares;538
14.6;C.6 Imperfect Evolutionary Systems;539
14.7;C.7 A Runtime Analysis of Evolutionary Algorithms for Constrained Optimization Problems;539
14.8;C.8 Classification with Ant Colony Optimization;540
14.9;C.9 Multiple Choices and Reputation in Multiagent Interactions;540
14.10;C.10 Coarse-grained Dynamics for Generalized Recombination;541
14.11;C.11 An Evolutionary Algorithm-based Approach to Automated Design of Analog and RF Circuits Using Adaptive Normalized Cost Functions;541
14.12;C.12 An Investigation on Noisy Environments in Evolutionary Multi- objective Optimization;542
14.13;C.13 Interactive Evolutionary Computation-based Hearing Aid Fitting;543
14.14;C.14 Evolutionary Development of Hierarchical Learning Structures;543
14.15;C.15 Knowledge Interaction with Genetic Programming in Mechatronic Systems Design Using Bond Graphs;544
14.16;C.16 A Distributed Evolutionary Classifier for Knowledge Discovery in Data Mining;544
14.17;C.17 Evolutionary Feature Synthesis for Object Recognition;544
14.18;C.18 Accelerating Evolutionary Algorithms with Gaussian Process Fitness Function Models;545
14.19;C.19 A Constraint-based Genetic Algorithm Approach for Mining Classification Rules;545
14.20;C.20 An Evolutionary Algorithm for Solving Nonlinear Bilevel Programming Based on a New Constraint- handling Scheme;546
14.21;C.21 Evolutionary Fuzzy Neural Networks for Hybrid Financial Prediction;546
14.22;C.22 Genetic Recurrent Fuzzy System by Coevolutionary Computation with Divide- and- Conquer Technique;547
14.23;C.23 Knowledge-based Fast Evaluation for Evolutionary Learning;547
14.24;C.24 A Comparative Study of Three Evolutionary Algorithms Incorporating Different Amounts of Domain Knowledge for Node Covering Problem;548
15;Appendix – D MATLAB Toolboxes;549
15.1;D.1 Genetic Algorithm and Direct Search Toolbox;549
15.2;D.2 Genetic and Evolutionary Algorithm Toolbox;550
15.3;D.3 Genetic Algorithm Toolbox;551
15.4;D.4 Genetic Programming Toolbox for MATLAB;552
16;Appendix – E Commercial Software Packages;553
16.1;E.1 ActiveGA;553
16.2;E.2 EnGENEer;553
16.3;E.3 EvoFrame;554
16.4;E.4 REALizer;555
16.5;E.5 Evolver;555
16.6;E.6 FlexTool;555
16.7;E.7 GAME;556
16.8;E.8 GeneHunter;556
16.9;E.9 Generator;556
16.10;E.10 Genetic Server and Genetic Library;557
16.11;E.11 MicroGA;558
16.12;E.12 Omega;558
16.13;E.13 OOGA;558
16.14;E.14 OptiGA;559
16.15;E.15 PC-Beagle;559
16.16;E.16 XpertRule GenAsys;559
16.17;E.17 XYpe;559
16.18;E.18 Evolution Machine;560
16.19;E.19 Evolutionary Objects;560
16.20;E.20 GAC, GAL;560
16.21;E.21 GAGA;560
16.22;E.22 GAGS;561
16.23;E.23 GAlib;561
16.24;E.24 GAWorkbench;561
16.25;E.25 Genesis;561
16.26;E.26 Genie;562
16.27;E.27 XGenetic;562
17;Appendix – F GA Source Codes in ‘C’ Language;563
17.1;F.1 A “Hello World” Genetic Algorithm Example;563
17.2;F.2 Test Function Using sin and cos;568
17.3;F.3 Using Matlab to Plot Data Generated by C Language;573
18;Appendix – G EC Class/ Code Libraries and Software Kits;575
18.1;G.1 EC Class/Code Libraries;575
18.2;ANNEvolve;575
18.3;daga;576
18.4;dgpf;576
18.5;Ease;576
18.6;EO;576
18.7;FORTRAN GA;577
18.8;GAlib: Matthew’s Genetic Algorithms Library;577
18.9;GALOPPS;577
18.10;GAS;578
18.11;GAUL;578
18.12;GECO;579
18.13;Genetic;579
18.14;GPdata;579
18.15;gpjpp Genetic Programming in Java;579
18.16;jaga;580
18.17;patched lil-gp;580
18.18;Lithos;580
18.19;Open BEAGLE;581
18.20;PGAPack;581
18.21;PIPE;581
18.22;pygene;582
18.23;Sugal;582
18.24;G.2 EC Software Kits/Applications;582
18.25;ADATE;582
18.26;esep & xesep;583
18.27;Corewars;583
18.28;Grany-3;583
18.29;JCASim;584
18.30;JGProg;584
19;Bibliography;585