Evolutionary Intelligence
An Introduction to Theory and Applications with Matlab
1st Edition
Published on 3. January 2008
22 pages
978-3-540-75382-7 (ISBN)
Description
"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."
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S. Sumathi | T. Hamsapriya | P. Surekha
Evolutionary Intelligence
An Introduction to Theory and Applications with Matlab
Book
01/2008
Springer
€106.95
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Content
1 - Preface [Seite 5]
1.1 - About the Book [Seite 6]
1.2 - Salient Features [Seite 6]
1.3 - Organization of the Book [Seite 6]
1.4 - About the Authors [Seite 7]
1.5 - Acknowledgement [Seite 9]
2 - Contents [Seite 10]
3 - Introduction to Evolutionary Computation [Seite 21]
3.1 - 1.1 Introduction [Seite 21]
3.2 - 1.2 Brief History [Seite 22]
3.3 - 1.3 Biological and Artificial Evolution [Seite 23]
3.4 - 1.3.1 EC Terminology [Seite 24]
3.5 - 1.3.2 Natural Evolution - The Inspiration from Biology [Seite 24]
3.6 - 1.4 Darwinian Evolution [Seite 25]
3.7 - 1.4.1 The Premise [Seite 26]
3.8 - 1.4.2 Natural Selection [Seite 26]
3.9 - 1.4.3 Slowly but Surely Process [Seite 27]
3.10 - 1.4.4 A Theory in Crisis [Seite 27]
3.11 - 1.4.5 Darwin's Theory of Evolution [Seite 28]
3.12 - 1.5 Genetics [Seite 29]
3.13 - 1.5.1 The Molecular Basis for Inheritance [Seite 29]
3.14 - 1.6 Evolutionary Computation [Seite 30]
3.15 - 1.7 Important Paradigms in Evolutionary Computation [Seite 32]
3.16 - 1.7.1 Genetic Algorithms [Seite 32]
3.17 - 1.7.2 Genetic Programming [Seite 34]
3.18 - 1.7.3 Evolutionary Programming [Seite 35]
3.19 - 1.7.4 Evolution Strategies [Seite 37]
3.20 - 1.8 Global Optimization [Seite 40]
3.21 - 1.9 Techniques of Global Optimization [Seite 41]
3.22 - 1.9.1 Branch and Bound [Seite 41]
3.23 - 1.9.2 Clustering Methods [Seite 42]
3.24 - 1.9.3 Hybrid Methods [Seite 42]
3.25 - 1.9.4 Simulated Annealing [Seite 46]
3.26 - 1.9.5 Statistical Global Optimization Algorithms [Seite 47]
3.27 - 1.9.6 Tabu Search [Seite 47]
3.28 - 1.9.7 Multi Objective Optimization [Seite 48]
3.29 - Summary [Seite 50]
3.30 - Review Questions [Seite 50]
4 - Principles of Evolutionary Algorithms [Seite 51]
4.1 - 2.1 Introduction [Seite 51]
4.2 - 2.2 Structure of Evolutionary Algorithms [Seite 52]
4.3 - 2.2.1 Illustration [Seite 54]
4.4 - 2.3 Components of Evolutionary Algorithms [Seite 56]
4.5 - 2.4 Representation [Seite 56]
4.6 - 2.5 Evaluation/Fitness Function [Seite 57]
4.7 - 2.6 Population Initialization [Seite 57]
4.8 - 2.7 Selection [Seite 58]
4.9 - 2.7.1 Rank Based Fitness Assignment [Seite 59]
4.10 - 2.7.2 Multi-objective Ranking [Seite 61]
4.11 - 2.7.3 Roulette Wheel selection [Seite 63]
4.12 - 2.7.4 Stochastic universal sampling [Seite 64]
4.13 - 2.7.5 Local Selection [Seite 65]
4.14 - 2.7.6 Truncation Selection [Seite 67]
4.15 - 2.7.7 Comparison of Selection Properties [Seite 69]
4.16 - 2.7.8 MATLAB Code Snippet for Selection [Seite 71]
4.17 - 2.8 Recombination [Seite 72]
4.18 - 2.8.1 Discrete Recombination [Seite 72]
4.19 - 2.8.2 Real Valued Recombination [Seite 73]
4.20 - 2.8.3 Binary Valued Recombination (Crossover) [Seite 77]
4.21 - 2.9 Mutation [Seite 81]
4.22 - 2.9.1 Real Valued Mutation [Seite 82]
4.23 - 2.9.2 Binary mutation [Seite 83]
4.24 - 2.9.3 Real Valued Mutation with Adaptation of Step Sizes [Seite 84]
4.25 - 2.9.4 Advanced Mutation [Seite 85]
4.26 - 2.9.5 Other Types of Mutation [Seite 86]
4.27 - 2.9.6 MATLAB Code Snippet for Mutation [Seite 87]
4.28 - 2.10 Reinsertion [Seite 87]
4.29 - 2.10.1 Global Reinsertion [Seite 87]
4.30 - 2.10.2 Local Reinsertion [Seite 88]
4.31 - 2.11 Reproduction Operator [Seite 89]
4.32 - 2.11.1 MATLAB Code Snippet for Reproduction [Seite 90]
4.33 - 2.12 Categorization of Parallel Evolutionary Algorithms [Seite 90]
4.34 - 2.13 Advantages of Evolutionary Algorithms [Seite 92]
4.35 - 2.14 Multi-objective Evolutionary Algorithms [Seite 93]
4.36 - 2.15 Critical Issues in Designing an Evolutionary Algorithm [Seite 94]
4.37 - Summary [Seite 95]
4.38 - Review Questions [Seite 95]
5 - Genetic Algorithms with Matlab [Seite 96]
5.1 - 3.1 Introduction [Seite 96]
5.2 - 3.2 History of Genetic Algorithm [Seite 99]
5.3 - 3.3 Genetic Algorithm Definition [Seite 100]
5.4 - 3.4 Models of Evolution [Seite 101]
5.5 - 3.5 Operational Functionality of Genetic Algorithms [Seite 102]
5.6 - 3.6 Genetic Algorithms - An Example [Seite 103]
5.7 - 3.7 Genetic Representation [Seite 105]
5.8 - 3.8 Genetic Algorithm Parameters [Seite 105]
5.9 - 3.8.1 Multi-Parameters [Seite 105]
5.10 - 3.8.2 Concatenated, Multi-Parameter, Mapped, Fixed- Point Coding [Seite 106]
5.11 - 3.8.3 Exploitable Techniques [Seite 106]
5.12 - 3.9 Schema Theorem and Theoretical Background [Seite 107]
5.13 - 3.9.1 Building Block Hypothesis [Seite 108]
5.14 - 3.9.2 Working of Genetic Algorithms [Seite 109]
5.15 - 3.9.3 Sets and Subsets [Seite 110]
5.16 - 3.9.4 The Dynamics of a Schema [Seite 111]
5.17 - 3.9.5 Compensating for Destructive Effects [Seite 112]
5.18 - 3.9.6 Mathematical Models [Seite 113]
5.19 - 3.9.7 Illustrations based on Schema Theorem [Seite 116]
5.20 - 3.10 Solving a Problem: Genotype and Fitness [Seite 119]
5.21 - 3.10.1 Non-Conventional Genotypes [Seite 121]
5.22 - 3.11 Advanced Operators in GA [Seite 123]
5.23 - 3.11.1 Inversion and Reordering [Seite 123]
5.24 - 3.11.2 Epistasis [Seite 123]
5.25 - 3.11.3 Deception [Seite 123]
5.26 - 3.11.4 Mutation and Naive Evolution [Seite 124]
5.27 - 3.11.5 Niche and Speciation [Seite 124]
5.28 - 3.11.6 Restricted Mating [Seite 124]
5.29 - 3.11.7 Diploidy and Dominance [Seite 125]
5.30 - 3.12 Important Issues in the Implementation of a GA [Seite 125]
5.31 - 3.13 Comparison of GA with Other Methods [Seite 126]
5.32 - 3.13.1 Neural Nets [Seite 126]
5.33 - 3.13.2 Random Search [Seite 126]
5.34 - 3.13.3 Gradient Methods [Seite 126]
5.35 - 3.13.4 Iterated Search [Seite 127]
5.36 - 3.13.5 Simulated Annealing [Seite 127]
5.37 - 3.14 Types of Genetic Algorithm [Seite 128]
5.38 - 3.14.1 Sequential GA [Seite 128]
5.39 - 3.14.2 Parallel GA [Seite 129]
5.40 - 3.14.3 Hybrid GA [Seite 131]
5.41 - 3.14.4 Adaptive GA [Seite 132]
5.42 - 3.14.5 Integrated Adaptive GA (IAGA) [Seite 135]
5.43 - 3.14.6 Messy GA [Seite 136]
5.44 - 3.14.7 Generational GA (GGA) [Seite 139]
5.45 - 3.14.8 Steady State GA (SSGA) [Seite 140]
5.46 - 3.15 Advantages of GA [Seite 141]
5.47 - 3.16 Matlab Examples of Genetic Algorithms 3.16.1 Genetic Algorithm Operations Implemented in MATLAB [Seite 142]
5.48 - Reproduction [Seite 142]
5.49 - Selection [Seite 142]
5.50 - Crossover [Seite 143]
5.51 - Fitness Function [Seite 144]
5.52 - Mutation [Seite 144]
5.53 - 3.16.2 Illustration 1 - Maximizing the given Function [Seite 145]
5.54 - 3.16.3 Illustration 2 - Optimization of a Multidimensional Non- Convex Function [Seite 151]
5.55 - 3.16.4 Illustration 3 - Traveling Salesman Problem [Seite 155]
5.56 - 3.16.5 Illustration 4 - GA using Float Representation [Seite 162]
5.57 - 3.16.6 Illustration 5 - Constrained Problem [Seite 177]
5.58 - 3.16.7 Illustration 6 - Maximum of any given Function [Seite 180]
5.59 - Summary [Seite 186]
5.60 - Review Questions [Seite 188]
6 - Genetic Programming Concepts [Seite 189]
6.1 - 4.1 Introduction [Seite 189]
6.2 - 4.2 A Brief History of Genetic Programming [Seite 193]
6.3 - 4.3 The Lisp Programming Language [Seite 194]
6.4 - 4.4 Operations of Genetic Programming [Seite 195]
6.5 - 4.4.1 Creating an Individual [Seite 195]
6.6 - 4.4.2 Creating a Random Population [Seite 196]
6.7 - 4.4.3 Fitness Test [Seite 197]
6.8 - 4.4.4 Functions and Terminals [Seite 197]
6.9 - 4.4.5 The Genetic Operations [Seite 197]
6.10 - 4.4.6 Selection Functions [Seite 198]
6.11 - 4.4.7 Crossover Operation [Seite 200]
6.12 - 4.4.8 Mutation [Seite 201]
6.13 - 4.4.9 User Decisions [Seite 201]
6.14 - 4.5 An Illustration [Seite 203]
6.15 - 4.6 The GP Paradigm in Machine Learning [Seite 204]
6.16 - 4.7 Preparatory Steps of Genetic Programming [Seite 206]
6.17 - 4.7.1 The Terminal Set [Seite 206]
6.18 - 4.7.2 The Function Set [Seite 207]
6.19 - 4.7.3 The Fitness Function [Seite 207]
6.20 - 4.7.4 The Algorithm Control Parameters [Seite 207]
6.21 - 4.7.5 The Termination Criterion [Seite 208]
6.22 - - [Seite 208]
6.23 - 4.8 Flow - Chart of Genetic Programming [Seite 209]
6.24 - 4.9 Type Constraints in Genetic Programming [Seite 211]
6.25 - 4.10 Enhanced Versions of Genetic Programming [Seite 213]
6.26 - 4.10.1 Meta-genetic Programming [Seite 214]
6.27 - 4.10.2 Cartesian Genetic Programming [Seite 219]
6.28 - 4.10.3 Strongly Typed Genetic Programming (STGP) [Seite 227]
6.29 - 4.11 Advantages of using Genetic Programming [Seite 235]
6.30 - Summary [Seite 235]
6.31 - Review Questions [Seite 236]
7 - Parallel Genetic Algorithms [Seite 237]
7.1 - 5.1 Introduction [Seite 237]
7.2 - 5.2 Parallel and Distributed Computer Architectures: An Overview [Seite 238]
7.3 - 5.3 Classification of PGA [Seite 241]
7.4 - 5.4 Parallel Population Models for Genetic Algorithms [Seite 242]
7.5 - 5.4.1 Classification of Global Population Models [Seite 243]
7.6 - 5.4.2 Global Population Models [Seite 244]
7.7 - 5.4.3 Regional Population Models [Seite 244]
7.8 - 5.4.4 Local Population Models [Seite 246]
7.9 - 5.5 Models Based on Distribution of Population [Seite 248]
7.10 - 5.5.1 Centralized PGA [Seite 248]
7.11 - 5.5.2 Distributed PGA [Seite 249]
7.12 - 5.6 PGA Models Based on Implementation [Seite 250]
7.13 - 5.6.1 Master-slave/Farming PGA [Seite 250]
7.14 - 5.6.2 Island PGA [Seite 252]
7.15 - 5.6.3 Cellular PGA [Seite 254]
7.16 - 5.7 PGA Models Based on Parallelism [Seite 256]
7.17 - 5.7.1 Global with Migration (coarse-grained) [Seite 256]
7.18 - 5.7.2 Global with Migration (fine-grained) [Seite 256]
7.19 - 5.8 Communication Topologies [Seite 258]
7.20 - 5.9 Hierarchical Parallel Algorithms [Seite 259]
7.21 - 5.10 Object Orientation (OO) and Parallelization [Seite 261]
7.22 - 5.11 Recent Advancements [Seite 262]
7.23 - 5.12 Advantages of Parallel Genetic Algorithms [Seite 264]
7.24 - Summary [Seite 265]
7.25 - Review Questions [Seite 265]
8 - Applications of Evolutionary Algorithms [Seite 267]
8.1 - 6.1 A Fingerprint Recognizer using Fuzzy Evolutionary Programming 6.1.1 Introduction [Seite 267]
8.2 - 6.1.2 Fingerprint Characteristics [Seite 268]
8.3 - 6.1.3 Fingerprint Recognition using EA [Seite 273]
8.4 - 6.1.4 Experimental Results [Seite 275]
8.5 - 6.1.5 Conclusion and Future Work [Seite 276]
8.6 - 6.2 An Evolutionary Programming Algorithm for Automatic Engineering Design 6.2.1 Introduction [Seite 276]
8.7 - 6.2.2 EPSOC: An Evolutionary Programming Algorithm using Self- Organized Criticality [Seite 278]
8.8 - 6.2.3 Case Studies [Seite 279]
8.9 - 6.2.4 Results of Numerical Experiments [Seite 281]
8.10 - 6.2.5 Conclusion [Seite 283]
8.11 - 6.3 Evolutionary Computing as a Tool for Grammar Development 6.3.1 Introduction [Seite 283]
8.12 - 6.3.2 Natural Language Grammar Development [Seite 284]
8.13 - 6.3.3 Grammar Evolution [Seite 285]
8.14 - 6.3.4 GRAEL-1: Probabilistic Grammar Optimization [Seite 286]
8.15 - 6.3.5 GRAEL-2: Grammar Rule Discovery [Seite 290]
8.16 - 6.3.6 GRAEL-3: Unsupervised Grammar Induction [Seite 292]
8.17 - 6.3.7 Concluding Remarks [Seite 293]
8.18 - 6.4 Waveform Synthesis using Evolutionary Computation 6.4.1 Introduction [Seite 294]
8.19 - 6.4.2 Evolutionary Manipulation of Waveforms [Seite 294]
8.20 - Crossover, Mutation and Fitness Evaluation [Seite 295]
8.21 - 6.4.3 Conclusion and Results [Seite 297]
8.22 - Appendix: Mathematical Model [Seite 298]
8.23 - 6.5 Scheduling Earth Observing Satellites with Evolutionary Algorithms 6.5.1 Introduction [Seite 300]
8.24 - 6.5.2 EOS Scheduling by Evolutionary Algorithms and other Optimization Techniques [Seite 302]
8.25 - 6.5.3 Results [Seite 304]
8.26 - 6.5.4 Future Work [Seite 306]
8.27 - 6.6 An Evolutionary Computation Approach to Scenario-based Risk- return Portfolio Optimization for General Risk Measures 6.6.1 Introduction [Seite 307]
8.28 - 6.6.2 Portfolio Optimization [Seite 307]
8.29 - 6.6.3 Evolutionary Portfolio Optimization [Seite 309]
8.30 - 6.6.4 Numerical Results [Seite 310]
8.31 - 6.6.5 Results [Seite 311]
8.32 - 6.6.6 Conclusion [Seite 314]
9 - Applications of Genetic Algorithms [Seite 315]
9.1 - 7.1 Assembly and Disassembly Planning by Using Fuzzy Logic & Genetic Algorithms [Seite 315]
9.2 - 7.1.1 Research Background [Seite 316]
9.3 - 7.1.2 Proposed Approach and Case Studies [Seite 321]
9.4 - 7.1.3 Discussion of Results [Seite 323]
9.5 - 7.1.4 Concluding Remarks [Seite 326]
9.6 - 7.2 Automatic Synthesis of Active Electronic Networks Using Genetic Algorithms [Seite 326]
9.7 - 7.2.1 Active Network Synthesis Using GAs [Seite 327]
9.8 - 7.2.2 Example of an Automatically-Synthesized Network [Seite 329]
9.9 - 7.2.3 Limitations of Automatic Network Synthesis [Seite 331]
9.10 - 7.2.4 Concluding Remarks [Seite 331]
9.11 - 7.3 A Genetic Algorithm for Mixed Macro and Standard Cell Placement [Seite 332]
9.12 - 7.3.1 Genetic Algorithm for Placement [Seite 332]
9.13 - 7.3.2 Experimental Results [Seite 336]
9.14 - 7.4 Knowledge Acquisition on Image Procssing Based on Genetic Algorithms [Seite 337]
9.15 - 7.4.1 Methods [Seite 338]
9.16 - 7.4.2 Results and Discussions [Seite 343]
9.17 - 7.4.3 Concluding Remarks [Seite 345]
9.18 - 7.5 Map Segmentation by Colour Cube Genetic K-Mean Clustering [Seite 345]
9.19 - 7.5.1 Genetic Clustering in Image Segmentation [Seite 346]
9.20 - 7.5.2 K-Means Clustering Model [Seite 347]
9.21 - 7.5.3 Genetic Implementation [Seite 347]
9.22 - 7.5.4 Results and Conclusions [Seite 348]
9.23 - 7.6 Genetic Algorithm-Based Performance Analysis of Self- Excited Induction Generator [Seite 349]
9.24 - 7.6.1 Modelling of SEIG System [Seite 350]
9.25 - 7.6.2 Genetic Algorithm Optimization [Seite 352]
9.26 - 7.6.3 Results and Discussion [Seite 353]
9.27 - 7.6.4 Concluding Remarks [Seite 355]
9.28 - 7.7 Feature Selection for Anns Using Genetic Algorithms in Condition Monitoring [Seite 356]
9.29 - 7.7.1 Signal Acquisition [Seite 358]
9.30 - 7.7.2 Neural Networks [Seite 358]
9.31 - 7.7.3 Genetic Algorithms [Seite 359]
9.32 - 7.7.4 Training and Simulation [Seite 359]
9.33 - 7.7.5 Results [Seite 360]
9.34 - 7.7.6 Concluding Remarks [Seite 361]
9.35 - 7.8 A Genetic Algorithm Approach to Scheduling Communications for a Class of Parallel Space-Time Adaptive Processing Algorithms [Seite 361]
9.36 - 7.8.1 Overview of Parallel STAP [Seite 362]
9.37 - 7.8.2 Genetic Algorithm Methodology [Seite 363]
9.38 - 7.8.3 Numerical Results [Seite 365]
9.39 - 7.8.4 Concluding Remarks [Seite 366]
9.40 - 7.9 A Multi-Objective Genetic Algorithm for on-Chip Real-Time Adaptation of a Multi- Carrier Based Telecommunications Receiver [Seite 367]
9.41 - 7.9.1 MC-CDMA Receiver [Seite 368]
9.42 - 7.9.2 Multi-objective Genetic Algorithm (GA) [Seite 368]
9.43 - 7.9.3 Results [Seite 371]
9.44 - 7.9.4 Concluding Remarks [Seite 373]
9.45 - 7.10 A VLSI Implementation of an Analog Neural Network Suited for Genetic Algorithms [Seite 373]
9.46 - 7.10.1 Realization of the Neural Network [Seite 375]
9.47 - 7.10.2 Implementation of the Genetic Training Algorithm [Seite 380]
9.48 - 7.10.3 Experimental Results [Seite 382]
9.49 - 7.10.4 Concluding Remarks [Seite 384]
10 - Genetic Programming Applications [Seite 385]
10.1 - 8.1 GP-Robocode: Using Genetic Programming to Evolve Robocode Players [Seite 385]
10.2 - 8.1.1 Robocode Rules [Seite 386]
10.3 - 8.1.2 Evolving Robocode Strategies using Genetic Programming [Seite 387]
10.4 - 8.1.3 Results [Seite 392]
10.5 - 8.1.4 Concluding Remarks [Seite 393]
10.6 - 8.2 Prediction of Biochemical Reactions using Genetic Programming [Seite 393]
10.7 - 8.2.1 Method and Results [Seite 394]
10.8 - 8.2.2 Discussion [Seite 395]
10.9 - 8.3 Application of Genetic Programming to High Energy Physics Event Selection [Seite 395]
10.10 - 8.3.1 Genetic Programming [Seite 396]
10.11 - 8.3.2 Combining Genetic Programming with High Energy Physics Data [Seite 398]
10.12 - 8.3.3 Selecting Genetic Programming Parameters [Seite 403]
10.13 - 8.3.4 Testing Genetic Programming on [Seite 407]
10.14 - 8.3.5 Concluding Remarks [Seite 412]
10.15 - 8.4 Using Genetic Programming to Generate Protocol Adaptors for Interprocess Communication [Seite 413]
10.16 - 8.4.1 Prerequisites of Interprocess Communication [Seite 415]
10.17 - 8.4.2 Specifying Protocols [Seite 415]
10.18 - 8.4.3 Evolving Protocols [Seite 418]
10.19 - 8.4.4 The Experiment [Seite 421]
10.20 - 8.4.5 Concluding Remarks [Seite 423]
10.21 - 8.5 Improving Technical Analysis Predictions: An Application of Genetic Programming [Seite 424]
10.22 - 8.5.1 Background [Seite 425]
10.23 - 8.5.2 FGP for Predication in DJIA Index [Seite 426]
10.24 - 8.5.3 Concluding Remarks [Seite 429]
10.25 - 8.6 Genetic Programming within Civil Engineering [Seite 430]
10.26 - 8.6.1 Generational Genetic Programming [Seite 430]
10.27 - 8.6.2 Applications of Genetic Programming in Civil Engineering [Seite 431]
10.28 - 8.6.3 Application of Genetic Programming in Structural Engineering [Seite 431]
10.29 - 8.6.4 Structural Encoding [Seite 431]
10.30 - 8.6.5 An Example of Structural Optimization [Seite 432]
10.31 - 8.6.6 10 Member Planar Truss [Seite 433]
10.32 - 8.6.7 Controller-GP Tableau [Seite 433]
10.33 - 8.6.8 Model [Seite 434]
10.34 - 8.6.9 View-Visualisation [Seite 435]
10.35 - 8.6.10 Concluding Remarks [Seite 437]
10.36 - 8.7 Chemical Process Controller Design using Genetic Programming [Seite 438]
10.37 - 8.7.1 Dynamic Reference Control Problem [Seite 438]
10.38 - 8.7.2 ARX Process Description [Seite 441]
10.39 - 8.7.3 CSTR (Continuous Stirred Tank Reactor) Process Description [Seite 441]
10.40 - 8.7.4 GP Problem Formulation [Seite 443]
10.41 - 8.7.5 GP Configuration and Implementation Aspects [Seite 444]
10.42 - 8.7.6 Results [Seite 446]
10.43 - 8.7.7 Concluding Remarks [Seite 448]
10.44 - 8.8 Trading Applications of Genetic Programming [Seite 449]
10.45 - 8.8.1 Application: Forecasting or Prediction [Seite 451]
10.46 - 8.8.2 Application: Finding Causal Relationships [Seite 452]
10.47 - 8.8.3 Application: Building Trading Rules [Seite 452]
10.48 - 8.8.4 Concluding Remarks [Seite 453]
10.49 - 8.9 Artificial Neural Network Development by Means of Genetic Programming with Graph Codification [Seite 453]
10.50 - 8.9.1 State of the Art [Seite 453]
10.51 - 8.9.2 Model [Seite 456]
10.52 - 8.9.3 Problems to be Solved [Seite 459]
10.53 - 8.9.4 Results and Comparison with Other Methods [Seite 459]
10.54 - 8.9.5 Concluding Remarks [Seite 461]
11 - Applications of Parallel Genetic Algorithm [Seite 462]
11.1 - 9.1 Timetabling Problem 9.1.1 Introduction [Seite 462]
11.2 - 9.1.2 Applying Genetic Algorithms to Timetabling [Seite 463]
11.3 - 9.1.3 A Parallel Algorithm [Seite 467]
11.4 - 9.1.4 Results [Seite 469]
11.5 - 9.1.5 Conclusion [Seite 470]
11.6 - 9.2 Assembling DNA Fragments with a Distributed Genetic Algorithm 9.2.1 Introduction [Seite 470]
11.7 - 9.2.2 The DNA Fragment Assembly Problem [Seite 471]
11.8 - 9.2.3 DNA Sequencing Process [Seite 472]
11.9 - 9.2.4 DNA Fragment Assembly Using the Sequential GA [Seite 474]
11.10 - 9.2.5 Implementation Details [Seite 475]
11.11 - 9.2.6 DNA Fragment Assembly Problem using the Parallel GA [Seite 477]
11.12 - 9.2.7 Experimental Results [Seite 479]
11.13 - Conclusions [Seite 485]
11.14 - 9.3 Investigating Parallel Genetic Algorithms on Job Shop Scheduling Problems 9.3.1 Introduction [Seite 486]
11.15 - 9.3.2 Job Shop Scheduling Problem [Seite 487]
11.16 - 9.3.3 Genetic Representation and Specific Operators [Seite 488]
11.17 - 9.3.4 Parallel Genetic Algorithms for JSSP [Seite 490]
11.18 - 9.3.5 Computational Results [Seite 492]
11.19 - The Effect of Parallelizing GAs [Seite 492]
11.20 - 9.3.6 Comparison of PGA Models [Seite 494]
11.21 - 9.4 Parallel Genetic Algorithm for Graph Coloring Problem 9.4.1 Introduction [Seite 496]
11.22 - Migration Model of Parallel Genetic Algorithm [Seite 496]
11.23 - 9.4.2 Genetic Operators for GCP [Seite 497]
11.24 - Sum-product Partition Crossover [Seite 497]
11.25 - 9.4.3 Experimental Verification [Seite 501]
11.26 - 9.4.4 Conclusion [Seite 503]
11.27 - 9.5 Robust and Distributed Genetic Algorithm for Ordering Problems 9.5.1 Introduction [Seite 503]
11.28 - 9.5.2 Ordering Problems [Seite 504]
11.29 - 9.5.3 Traveling Salesman Problem [Seite 505]
11.30 - 9.5.4 Distributed Genetic Algorithm [Seite 508]
11.31 - Results for Hamiltonian Cycle TSP [Seite 516]
11.32 - Results for Oliver's Hamiltonian Cycle TSP [Seite 517]
11.33 - Conclusion [Seite 519]
12 - Appendix - A Glossary [Seite 520]
12.1 - A [Seite 520]
12.2 - B [Seite 520]
12.3 - C [Seite 521]
12.4 - D [Seite 522]
12.5 - E [Seite 523]
12.6 - F [Seite 525]
12.7 - G [Seite 525]
12.8 - H [Seite 527]
12.9 - I [Seite 527]
12.10 - L [Seite 527]
12.11 - M [Seite 528]
12.12 - N [Seite 528]
12.13 - O [Seite 529]
12.14 - P [Seite 530]
12.15 - R [Seite 530]
12.16 - S [Seite 531]
12.17 - T [Seite 532]
12.18 - V [Seite 533]
13 - Appendix - B Abbreviations [Seite 534]
14 - Appendix - C Research Projects [Seite 537]
14.1 - C.1 Evolutionary Simulation-based Validation [Seite 537]
14.2 - C.2 Automatic Generation of Validation Stimuli for Application- specific Processors [Seite 537]
14.3 - C.3 Dynamic Prediction ofWeb Requests [Seite 538]
14.4 - C.4 Analog Genetic Encoding for the Evolution of Circuits and Networks [Seite 538]
14.5 - C.5 An Evolutionary Algorithm for Global Optimization Based on Level- set Evolution and Latin Squares [Seite 538]
14.6 - C.6 Imperfect Evolutionary Systems [Seite 539]
14.7 - C.7 A Runtime Analysis of Evolutionary Algorithms for Constrained Optimization Problems [Seite 539]
14.8 - C.8 Classification with Ant Colony Optimization [Seite 540]
14.9 - C.9 Multiple Choices and Reputation in Multiagent Interactions [Seite 540]
14.10 - C.10 Coarse-grained Dynamics for Generalized Recombination [Seite 541]
14.11 - C.11 An Evolutionary Algorithm-based Approach to Automated Design of Analog and RF Circuits Using Adaptive Normalized Cost Functions [Seite 541]
14.12 - C.12 An Investigation on Noisy Environments in Evolutionary Multi- objective Optimization [Seite 542]
14.13 - C.13 Interactive Evolutionary Computation-based Hearing Aid Fitting [Seite 543]
14.14 - C.14 Evolutionary Development of Hierarchical Learning Structures [Seite 543]
14.15 - C.15 Knowledge Interaction with Genetic Programming in Mechatronic Systems Design Using Bond Graphs [Seite 544]
14.16 - C.16 A Distributed Evolutionary Classifier for Knowledge Discovery in Data Mining [Seite 544]
14.17 - C.17 Evolutionary Feature Synthesis for Object Recognition [Seite 544]
14.18 - C.18 Accelerating Evolutionary Algorithms with Gaussian Process Fitness Function Models [Seite 545]
14.19 - C.19 A Constraint-based Genetic Algorithm Approach for Mining Classification Rules [Seite 545]
14.20 - C.20 An Evolutionary Algorithm for Solving Nonlinear Bilevel Programming Based on a New Constraint- handling Scheme [Seite 546]
14.21 - C.21 Evolutionary Fuzzy Neural Networks for Hybrid Financial Prediction [Seite 546]
14.22 - C.22 Genetic Recurrent Fuzzy System by Coevolutionary Computation with Divide- and- Conquer Technique [Seite 547]
14.23 - C.23 Knowledge-based Fast Evaluation for Evolutionary Learning [Seite 547]
14.24 - C.24 A Comparative Study of Three Evolutionary Algorithms Incorporating Different Amounts of Domain Knowledge for Node Covering Problem [Seite 548]
15 - Appendix - D MATLAB Toolboxes [Seite 549]
15.1 - D.1 Genetic Algorithm and Direct Search Toolbox [Seite 549]
15.2 - D.2 Genetic and Evolutionary Algorithm Toolbox [Seite 550]
15.3 - D.3 Genetic Algorithm Toolbox [Seite 551]
15.4 - D.4 Genetic Programming Toolbox for MATLAB [Seite 552]
16 - Appendix - E Commercial Software Packages [Seite 553]
16.1 - E.1 ActiveGA [Seite 553]
16.2 - E.2 EnGENEer [Seite 553]
16.3 - E.3 EvoFrame [Seite 554]
16.4 - E.4 REALizer [Seite 555]
16.5 - E.5 Evolver [Seite 555]
16.6 - E.6 FlexTool [Seite 555]
16.7 - E.7 GAME [Seite 556]
16.8 - E.8 GeneHunter [Seite 556]
16.9 - E.9 Generator [Seite 556]
16.10 - E.10 Genetic Server and Genetic Library [Seite 557]
16.11 - E.11 MicroGA [Seite 558]
16.12 - E.12 Omega [Seite 558]
16.13 - E.13 OOGA [Seite 558]
16.14 - E.14 OptiGA [Seite 559]
16.15 - E.15 PC-Beagle [Seite 559]
16.16 - E.16 XpertRule GenAsys [Seite 559]
16.17 - E.17 XYpe [Seite 559]
16.18 - E.18 Evolution Machine [Seite 560]
16.19 - E.19 Evolutionary Objects [Seite 560]
16.20 - E.20 GAC, GAL [Seite 560]
16.21 - E.21 GAGA [Seite 560]
16.22 - E.22 GAGS [Seite 561]
16.23 - E.23 GAlib [Seite 561]
16.24 - E.24 GAWorkbench [Seite 561]
16.25 - E.25 Genesis [Seite 561]
16.26 - E.26 Genie [Seite 562]
16.27 - E.27 XGenetic [Seite 562]
17 - Appendix - F GA Source Codes in 'C' Language [Seite 563]
17.1 - F.1 A "Hello World" Genetic Algorithm Example [Seite 563]
17.2 - F.2 Test Function Using sin and cos [Seite 568]
17.3 - F.3 Using Matlab to Plot Data Generated by C Language [Seite 573]
18 - Appendix - G EC Class/ Code Libraries and Software Kits [Seite 575]
18.1 - G.1 EC Class/Code Libraries [Seite 575]
18.2 - ANNEvolve [Seite 575]
18.3 - daga [Seite 576]
18.4 - dgpf [Seite 576]
18.5 - Ease [Seite 576]
18.6 - EO [Seite 576]
18.7 - FORTRAN GA [Seite 577]
18.8 - GAlib: Matthew's Genetic Algorithms Library [Seite 577]
18.9 - GALOPPS [Seite 577]
18.10 - GAS [Seite 578]
18.11 - GAUL [Seite 578]
18.12 - GECO [Seite 579]
18.13 - Genetic [Seite 579]
18.14 - GPdata [Seite 579]
18.15 - gpjpp Genetic Programming in Java [Seite 579]
18.16 - jaga [Seite 580]
18.17 - patched lil-gp [Seite 580]
18.18 - Lithos [Seite 580]
18.19 - Open BEAGLE [Seite 581]
18.20 - PGAPack [Seite 581]
18.21 - PIPE [Seite 581]
18.22 - pygene [Seite 582]
18.23 - Sugal [Seite 582]
18.24 - G.2 EC Software Kits/Applications [Seite 582]
18.25 - ADATE [Seite 582]
18.26 - esep & xesep [Seite 583]
18.27 - Corewars [Seite 583]
18.28 - Grany-3 [Seite 583]
18.29 - JCASim [Seite 584]
18.30 - JGProg [Seite 584]
19 - Bibliography [Seite 585]
1.1 - About the Book [Seite 6]
1.2 - Salient Features [Seite 6]
1.3 - Organization of the Book [Seite 6]
1.4 - About the Authors [Seite 7]
1.5 - Acknowledgement [Seite 9]
2 - Contents [Seite 10]
3 - Introduction to Evolutionary Computation [Seite 21]
3.1 - 1.1 Introduction [Seite 21]
3.2 - 1.2 Brief History [Seite 22]
3.3 - 1.3 Biological and Artificial Evolution [Seite 23]
3.4 - 1.3.1 EC Terminology [Seite 24]
3.5 - 1.3.2 Natural Evolution - The Inspiration from Biology [Seite 24]
3.6 - 1.4 Darwinian Evolution [Seite 25]
3.7 - 1.4.1 The Premise [Seite 26]
3.8 - 1.4.2 Natural Selection [Seite 26]
3.9 - 1.4.3 Slowly but Surely Process [Seite 27]
3.10 - 1.4.4 A Theory in Crisis [Seite 27]
3.11 - 1.4.5 Darwin's Theory of Evolution [Seite 28]
3.12 - 1.5 Genetics [Seite 29]
3.13 - 1.5.1 The Molecular Basis for Inheritance [Seite 29]
3.14 - 1.6 Evolutionary Computation [Seite 30]
3.15 - 1.7 Important Paradigms in Evolutionary Computation [Seite 32]
3.16 - 1.7.1 Genetic Algorithms [Seite 32]
3.17 - 1.7.2 Genetic Programming [Seite 34]
3.18 - 1.7.3 Evolutionary Programming [Seite 35]
3.19 - 1.7.4 Evolution Strategies [Seite 37]
3.20 - 1.8 Global Optimization [Seite 40]
3.21 - 1.9 Techniques of Global Optimization [Seite 41]
3.22 - 1.9.1 Branch and Bound [Seite 41]
3.23 - 1.9.2 Clustering Methods [Seite 42]
3.24 - 1.9.3 Hybrid Methods [Seite 42]
3.25 - 1.9.4 Simulated Annealing [Seite 46]
3.26 - 1.9.5 Statistical Global Optimization Algorithms [Seite 47]
3.27 - 1.9.6 Tabu Search [Seite 47]
3.28 - 1.9.7 Multi Objective Optimization [Seite 48]
3.29 - Summary [Seite 50]
3.30 - Review Questions [Seite 50]
4 - Principles of Evolutionary Algorithms [Seite 51]
4.1 - 2.1 Introduction [Seite 51]
4.2 - 2.2 Structure of Evolutionary Algorithms [Seite 52]
4.3 - 2.2.1 Illustration [Seite 54]
4.4 - 2.3 Components of Evolutionary Algorithms [Seite 56]
4.5 - 2.4 Representation [Seite 56]
4.6 - 2.5 Evaluation/Fitness Function [Seite 57]
4.7 - 2.6 Population Initialization [Seite 57]
4.8 - 2.7 Selection [Seite 58]
4.9 - 2.7.1 Rank Based Fitness Assignment [Seite 59]
4.10 - 2.7.2 Multi-objective Ranking [Seite 61]
4.11 - 2.7.3 Roulette Wheel selection [Seite 63]
4.12 - 2.7.4 Stochastic universal sampling [Seite 64]
4.13 - 2.7.5 Local Selection [Seite 65]
4.14 - 2.7.6 Truncation Selection [Seite 67]
4.15 - 2.7.7 Comparison of Selection Properties [Seite 69]
4.16 - 2.7.8 MATLAB Code Snippet for Selection [Seite 71]
4.17 - 2.8 Recombination [Seite 72]
4.18 - 2.8.1 Discrete Recombination [Seite 72]
4.19 - 2.8.2 Real Valued Recombination [Seite 73]
4.20 - 2.8.3 Binary Valued Recombination (Crossover) [Seite 77]
4.21 - 2.9 Mutation [Seite 81]
4.22 - 2.9.1 Real Valued Mutation [Seite 82]
4.23 - 2.9.2 Binary mutation [Seite 83]
4.24 - 2.9.3 Real Valued Mutation with Adaptation of Step Sizes [Seite 84]
4.25 - 2.9.4 Advanced Mutation [Seite 85]
4.26 - 2.9.5 Other Types of Mutation [Seite 86]
4.27 - 2.9.6 MATLAB Code Snippet for Mutation [Seite 87]
4.28 - 2.10 Reinsertion [Seite 87]
4.29 - 2.10.1 Global Reinsertion [Seite 87]
4.30 - 2.10.2 Local Reinsertion [Seite 88]
4.31 - 2.11 Reproduction Operator [Seite 89]
4.32 - 2.11.1 MATLAB Code Snippet for Reproduction [Seite 90]
4.33 - 2.12 Categorization of Parallel Evolutionary Algorithms [Seite 90]
4.34 - 2.13 Advantages of Evolutionary Algorithms [Seite 92]
4.35 - 2.14 Multi-objective Evolutionary Algorithms [Seite 93]
4.36 - 2.15 Critical Issues in Designing an Evolutionary Algorithm [Seite 94]
4.37 - Summary [Seite 95]
4.38 - Review Questions [Seite 95]
5 - Genetic Algorithms with Matlab [Seite 96]
5.1 - 3.1 Introduction [Seite 96]
5.2 - 3.2 History of Genetic Algorithm [Seite 99]
5.3 - 3.3 Genetic Algorithm Definition [Seite 100]
5.4 - 3.4 Models of Evolution [Seite 101]
5.5 - 3.5 Operational Functionality of Genetic Algorithms [Seite 102]
5.6 - 3.6 Genetic Algorithms - An Example [Seite 103]
5.7 - 3.7 Genetic Representation [Seite 105]
5.8 - 3.8 Genetic Algorithm Parameters [Seite 105]
5.9 - 3.8.1 Multi-Parameters [Seite 105]
5.10 - 3.8.2 Concatenated, Multi-Parameter, Mapped, Fixed- Point Coding [Seite 106]
5.11 - 3.8.3 Exploitable Techniques [Seite 106]
5.12 - 3.9 Schema Theorem and Theoretical Background [Seite 107]
5.13 - 3.9.1 Building Block Hypothesis [Seite 108]
5.14 - 3.9.2 Working of Genetic Algorithms [Seite 109]
5.15 - 3.9.3 Sets and Subsets [Seite 110]
5.16 - 3.9.4 The Dynamics of a Schema [Seite 111]
5.17 - 3.9.5 Compensating for Destructive Effects [Seite 112]
5.18 - 3.9.6 Mathematical Models [Seite 113]
5.19 - 3.9.7 Illustrations based on Schema Theorem [Seite 116]
5.20 - 3.10 Solving a Problem: Genotype and Fitness [Seite 119]
5.21 - 3.10.1 Non-Conventional Genotypes [Seite 121]
5.22 - 3.11 Advanced Operators in GA [Seite 123]
5.23 - 3.11.1 Inversion and Reordering [Seite 123]
5.24 - 3.11.2 Epistasis [Seite 123]
5.25 - 3.11.3 Deception [Seite 123]
5.26 - 3.11.4 Mutation and Naive Evolution [Seite 124]
5.27 - 3.11.5 Niche and Speciation [Seite 124]
5.28 - 3.11.6 Restricted Mating [Seite 124]
5.29 - 3.11.7 Diploidy and Dominance [Seite 125]
5.30 - 3.12 Important Issues in the Implementation of a GA [Seite 125]
5.31 - 3.13 Comparison of GA with Other Methods [Seite 126]
5.32 - 3.13.1 Neural Nets [Seite 126]
5.33 - 3.13.2 Random Search [Seite 126]
5.34 - 3.13.3 Gradient Methods [Seite 126]
5.35 - 3.13.4 Iterated Search [Seite 127]
5.36 - 3.13.5 Simulated Annealing [Seite 127]
5.37 - 3.14 Types of Genetic Algorithm [Seite 128]
5.38 - 3.14.1 Sequential GA [Seite 128]
5.39 - 3.14.2 Parallel GA [Seite 129]
5.40 - 3.14.3 Hybrid GA [Seite 131]
5.41 - 3.14.4 Adaptive GA [Seite 132]
5.42 - 3.14.5 Integrated Adaptive GA (IAGA) [Seite 135]
5.43 - 3.14.6 Messy GA [Seite 136]
5.44 - 3.14.7 Generational GA (GGA) [Seite 139]
5.45 - 3.14.8 Steady State GA (SSGA) [Seite 140]
5.46 - 3.15 Advantages of GA [Seite 141]
5.47 - 3.16 Matlab Examples of Genetic Algorithms 3.16.1 Genetic Algorithm Operations Implemented in MATLAB [Seite 142]
5.48 - Reproduction [Seite 142]
5.49 - Selection [Seite 142]
5.50 - Crossover [Seite 143]
5.51 - Fitness Function [Seite 144]
5.52 - Mutation [Seite 144]
5.53 - 3.16.2 Illustration 1 - Maximizing the given Function [Seite 145]
5.54 - 3.16.3 Illustration 2 - Optimization of a Multidimensional Non- Convex Function [Seite 151]
5.55 - 3.16.4 Illustration 3 - Traveling Salesman Problem [Seite 155]
5.56 - 3.16.5 Illustration 4 - GA using Float Representation [Seite 162]
5.57 - 3.16.6 Illustration 5 - Constrained Problem [Seite 177]
5.58 - 3.16.7 Illustration 6 - Maximum of any given Function [Seite 180]
5.59 - Summary [Seite 186]
5.60 - Review Questions [Seite 188]
6 - Genetic Programming Concepts [Seite 189]
6.1 - 4.1 Introduction [Seite 189]
6.2 - 4.2 A Brief History of Genetic Programming [Seite 193]
6.3 - 4.3 The Lisp Programming Language [Seite 194]
6.4 - 4.4 Operations of Genetic Programming [Seite 195]
6.5 - 4.4.1 Creating an Individual [Seite 195]
6.6 - 4.4.2 Creating a Random Population [Seite 196]
6.7 - 4.4.3 Fitness Test [Seite 197]
6.8 - 4.4.4 Functions and Terminals [Seite 197]
6.9 - 4.4.5 The Genetic Operations [Seite 197]
6.10 - 4.4.6 Selection Functions [Seite 198]
6.11 - 4.4.7 Crossover Operation [Seite 200]
6.12 - 4.4.8 Mutation [Seite 201]
6.13 - 4.4.9 User Decisions [Seite 201]
6.14 - 4.5 An Illustration [Seite 203]
6.15 - 4.6 The GP Paradigm in Machine Learning [Seite 204]
6.16 - 4.7 Preparatory Steps of Genetic Programming [Seite 206]
6.17 - 4.7.1 The Terminal Set [Seite 206]
6.18 - 4.7.2 The Function Set [Seite 207]
6.19 - 4.7.3 The Fitness Function [Seite 207]
6.20 - 4.7.4 The Algorithm Control Parameters [Seite 207]
6.21 - 4.7.5 The Termination Criterion [Seite 208]
6.22 - - [Seite 208]
6.23 - 4.8 Flow - Chart of Genetic Programming [Seite 209]
6.24 - 4.9 Type Constraints in Genetic Programming [Seite 211]
6.25 - 4.10 Enhanced Versions of Genetic Programming [Seite 213]
6.26 - 4.10.1 Meta-genetic Programming [Seite 214]
6.27 - 4.10.2 Cartesian Genetic Programming [Seite 219]
6.28 - 4.10.3 Strongly Typed Genetic Programming (STGP) [Seite 227]
6.29 - 4.11 Advantages of using Genetic Programming [Seite 235]
6.30 - Summary [Seite 235]
6.31 - Review Questions [Seite 236]
7 - Parallel Genetic Algorithms [Seite 237]
7.1 - 5.1 Introduction [Seite 237]
7.2 - 5.2 Parallel and Distributed Computer Architectures: An Overview [Seite 238]
7.3 - 5.3 Classification of PGA [Seite 241]
7.4 - 5.4 Parallel Population Models for Genetic Algorithms [Seite 242]
7.5 - 5.4.1 Classification of Global Population Models [Seite 243]
7.6 - 5.4.2 Global Population Models [Seite 244]
7.7 - 5.4.3 Regional Population Models [Seite 244]
7.8 - 5.4.4 Local Population Models [Seite 246]
7.9 - 5.5 Models Based on Distribution of Population [Seite 248]
7.10 - 5.5.1 Centralized PGA [Seite 248]
7.11 - 5.5.2 Distributed PGA [Seite 249]
7.12 - 5.6 PGA Models Based on Implementation [Seite 250]
7.13 - 5.6.1 Master-slave/Farming PGA [Seite 250]
7.14 - 5.6.2 Island PGA [Seite 252]
7.15 - 5.6.3 Cellular PGA [Seite 254]
7.16 - 5.7 PGA Models Based on Parallelism [Seite 256]
7.17 - 5.7.1 Global with Migration (coarse-grained) [Seite 256]
7.18 - 5.7.2 Global with Migration (fine-grained) [Seite 256]
7.19 - 5.8 Communication Topologies [Seite 258]
7.20 - 5.9 Hierarchical Parallel Algorithms [Seite 259]
7.21 - 5.10 Object Orientation (OO) and Parallelization [Seite 261]
7.22 - 5.11 Recent Advancements [Seite 262]
7.23 - 5.12 Advantages of Parallel Genetic Algorithms [Seite 264]
7.24 - Summary [Seite 265]
7.25 - Review Questions [Seite 265]
8 - Applications of Evolutionary Algorithms [Seite 267]
8.1 - 6.1 A Fingerprint Recognizer using Fuzzy Evolutionary Programming 6.1.1 Introduction [Seite 267]
8.2 - 6.1.2 Fingerprint Characteristics [Seite 268]
8.3 - 6.1.3 Fingerprint Recognition using EA [Seite 273]
8.4 - 6.1.4 Experimental Results [Seite 275]
8.5 - 6.1.5 Conclusion and Future Work [Seite 276]
8.6 - 6.2 An Evolutionary Programming Algorithm for Automatic Engineering Design 6.2.1 Introduction [Seite 276]
8.7 - 6.2.2 EPSOC: An Evolutionary Programming Algorithm using Self- Organized Criticality [Seite 278]
8.8 - 6.2.3 Case Studies [Seite 279]
8.9 - 6.2.4 Results of Numerical Experiments [Seite 281]
8.10 - 6.2.5 Conclusion [Seite 283]
8.11 - 6.3 Evolutionary Computing as a Tool for Grammar Development 6.3.1 Introduction [Seite 283]
8.12 - 6.3.2 Natural Language Grammar Development [Seite 284]
8.13 - 6.3.3 Grammar Evolution [Seite 285]
8.14 - 6.3.4 GRAEL-1: Probabilistic Grammar Optimization [Seite 286]
8.15 - 6.3.5 GRAEL-2: Grammar Rule Discovery [Seite 290]
8.16 - 6.3.6 GRAEL-3: Unsupervised Grammar Induction [Seite 292]
8.17 - 6.3.7 Concluding Remarks [Seite 293]
8.18 - 6.4 Waveform Synthesis using Evolutionary Computation 6.4.1 Introduction [Seite 294]
8.19 - 6.4.2 Evolutionary Manipulation of Waveforms [Seite 294]
8.20 - Crossover, Mutation and Fitness Evaluation [Seite 295]
8.21 - 6.4.3 Conclusion and Results [Seite 297]
8.22 - Appendix: Mathematical Model [Seite 298]
8.23 - 6.5 Scheduling Earth Observing Satellites with Evolutionary Algorithms 6.5.1 Introduction [Seite 300]
8.24 - 6.5.2 EOS Scheduling by Evolutionary Algorithms and other Optimization Techniques [Seite 302]
8.25 - 6.5.3 Results [Seite 304]
8.26 - 6.5.4 Future Work [Seite 306]
8.27 - 6.6 An Evolutionary Computation Approach to Scenario-based Risk- return Portfolio Optimization for General Risk Measures 6.6.1 Introduction [Seite 307]
8.28 - 6.6.2 Portfolio Optimization [Seite 307]
8.29 - 6.6.3 Evolutionary Portfolio Optimization [Seite 309]
8.30 - 6.6.4 Numerical Results [Seite 310]
8.31 - 6.6.5 Results [Seite 311]
8.32 - 6.6.6 Conclusion [Seite 314]
9 - Applications of Genetic Algorithms [Seite 315]
9.1 - 7.1 Assembly and Disassembly Planning by Using Fuzzy Logic & Genetic Algorithms [Seite 315]
9.2 - 7.1.1 Research Background [Seite 316]
9.3 - 7.1.2 Proposed Approach and Case Studies [Seite 321]
9.4 - 7.1.3 Discussion of Results [Seite 323]
9.5 - 7.1.4 Concluding Remarks [Seite 326]
9.6 - 7.2 Automatic Synthesis of Active Electronic Networks Using Genetic Algorithms [Seite 326]
9.7 - 7.2.1 Active Network Synthesis Using GAs [Seite 327]
9.8 - 7.2.2 Example of an Automatically-Synthesized Network [Seite 329]
9.9 - 7.2.3 Limitations of Automatic Network Synthesis [Seite 331]
9.10 - 7.2.4 Concluding Remarks [Seite 331]
9.11 - 7.3 A Genetic Algorithm for Mixed Macro and Standard Cell Placement [Seite 332]
9.12 - 7.3.1 Genetic Algorithm for Placement [Seite 332]
9.13 - 7.3.2 Experimental Results [Seite 336]
9.14 - 7.4 Knowledge Acquisition on Image Procssing Based on Genetic Algorithms [Seite 337]
9.15 - 7.4.1 Methods [Seite 338]
9.16 - 7.4.2 Results and Discussions [Seite 343]
9.17 - 7.4.3 Concluding Remarks [Seite 345]
9.18 - 7.5 Map Segmentation by Colour Cube Genetic K-Mean Clustering [Seite 345]
9.19 - 7.5.1 Genetic Clustering in Image Segmentation [Seite 346]
9.20 - 7.5.2 K-Means Clustering Model [Seite 347]
9.21 - 7.5.3 Genetic Implementation [Seite 347]
9.22 - 7.5.4 Results and Conclusions [Seite 348]
9.23 - 7.6 Genetic Algorithm-Based Performance Analysis of Self- Excited Induction Generator [Seite 349]
9.24 - 7.6.1 Modelling of SEIG System [Seite 350]
9.25 - 7.6.2 Genetic Algorithm Optimization [Seite 352]
9.26 - 7.6.3 Results and Discussion [Seite 353]
9.27 - 7.6.4 Concluding Remarks [Seite 355]
9.28 - 7.7 Feature Selection for Anns Using Genetic Algorithms in Condition Monitoring [Seite 356]
9.29 - 7.7.1 Signal Acquisition [Seite 358]
9.30 - 7.7.2 Neural Networks [Seite 358]
9.31 - 7.7.3 Genetic Algorithms [Seite 359]
9.32 - 7.7.4 Training and Simulation [Seite 359]
9.33 - 7.7.5 Results [Seite 360]
9.34 - 7.7.6 Concluding Remarks [Seite 361]
9.35 - 7.8 A Genetic Algorithm Approach to Scheduling Communications for a Class of Parallel Space-Time Adaptive Processing Algorithms [Seite 361]
9.36 - 7.8.1 Overview of Parallel STAP [Seite 362]
9.37 - 7.8.2 Genetic Algorithm Methodology [Seite 363]
9.38 - 7.8.3 Numerical Results [Seite 365]
9.39 - 7.8.4 Concluding Remarks [Seite 366]
9.40 - 7.9 A Multi-Objective Genetic Algorithm for on-Chip Real-Time Adaptation of a Multi- Carrier Based Telecommunications Receiver [Seite 367]
9.41 - 7.9.1 MC-CDMA Receiver [Seite 368]
9.42 - 7.9.2 Multi-objective Genetic Algorithm (GA) [Seite 368]
9.43 - 7.9.3 Results [Seite 371]
9.44 - 7.9.4 Concluding Remarks [Seite 373]
9.45 - 7.10 A VLSI Implementation of an Analog Neural Network Suited for Genetic Algorithms [Seite 373]
9.46 - 7.10.1 Realization of the Neural Network [Seite 375]
9.47 - 7.10.2 Implementation of the Genetic Training Algorithm [Seite 380]
9.48 - 7.10.3 Experimental Results [Seite 382]
9.49 - 7.10.4 Concluding Remarks [Seite 384]
10 - Genetic Programming Applications [Seite 385]
10.1 - 8.1 GP-Robocode: Using Genetic Programming to Evolve Robocode Players [Seite 385]
10.2 - 8.1.1 Robocode Rules [Seite 386]
10.3 - 8.1.2 Evolving Robocode Strategies using Genetic Programming [Seite 387]
10.4 - 8.1.3 Results [Seite 392]
10.5 - 8.1.4 Concluding Remarks [Seite 393]
10.6 - 8.2 Prediction of Biochemical Reactions using Genetic Programming [Seite 393]
10.7 - 8.2.1 Method and Results [Seite 394]
10.8 - 8.2.2 Discussion [Seite 395]
10.9 - 8.3 Application of Genetic Programming to High Energy Physics Event Selection [Seite 395]
10.10 - 8.3.1 Genetic Programming [Seite 396]
10.11 - 8.3.2 Combining Genetic Programming with High Energy Physics Data [Seite 398]
10.12 - 8.3.3 Selecting Genetic Programming Parameters [Seite 403]
10.13 - 8.3.4 Testing Genetic Programming on [Seite 407]
10.14 - 8.3.5 Concluding Remarks [Seite 412]
10.15 - 8.4 Using Genetic Programming to Generate Protocol Adaptors for Interprocess Communication [Seite 413]
10.16 - 8.4.1 Prerequisites of Interprocess Communication [Seite 415]
10.17 - 8.4.2 Specifying Protocols [Seite 415]
10.18 - 8.4.3 Evolving Protocols [Seite 418]
10.19 - 8.4.4 The Experiment [Seite 421]
10.20 - 8.4.5 Concluding Remarks [Seite 423]
10.21 - 8.5 Improving Technical Analysis Predictions: An Application of Genetic Programming [Seite 424]
10.22 - 8.5.1 Background [Seite 425]
10.23 - 8.5.2 FGP for Predication in DJIA Index [Seite 426]
10.24 - 8.5.3 Concluding Remarks [Seite 429]
10.25 - 8.6 Genetic Programming within Civil Engineering [Seite 430]
10.26 - 8.6.1 Generational Genetic Programming [Seite 430]
10.27 - 8.6.2 Applications of Genetic Programming in Civil Engineering [Seite 431]
10.28 - 8.6.3 Application of Genetic Programming in Structural Engineering [Seite 431]
10.29 - 8.6.4 Structural Encoding [Seite 431]
10.30 - 8.6.5 An Example of Structural Optimization [Seite 432]
10.31 - 8.6.6 10 Member Planar Truss [Seite 433]
10.32 - 8.6.7 Controller-GP Tableau [Seite 433]
10.33 - 8.6.8 Model [Seite 434]
10.34 - 8.6.9 View-Visualisation [Seite 435]
10.35 - 8.6.10 Concluding Remarks [Seite 437]
10.36 - 8.7 Chemical Process Controller Design using Genetic Programming [Seite 438]
10.37 - 8.7.1 Dynamic Reference Control Problem [Seite 438]
10.38 - 8.7.2 ARX Process Description [Seite 441]
10.39 - 8.7.3 CSTR (Continuous Stirred Tank Reactor) Process Description [Seite 441]
10.40 - 8.7.4 GP Problem Formulation [Seite 443]
10.41 - 8.7.5 GP Configuration and Implementation Aspects [Seite 444]
10.42 - 8.7.6 Results [Seite 446]
10.43 - 8.7.7 Concluding Remarks [Seite 448]
10.44 - 8.8 Trading Applications of Genetic Programming [Seite 449]
10.45 - 8.8.1 Application: Forecasting or Prediction [Seite 451]
10.46 - 8.8.2 Application: Finding Causal Relationships [Seite 452]
10.47 - 8.8.3 Application: Building Trading Rules [Seite 452]
10.48 - 8.8.4 Concluding Remarks [Seite 453]
10.49 - 8.9 Artificial Neural Network Development by Means of Genetic Programming with Graph Codification [Seite 453]
10.50 - 8.9.1 State of the Art [Seite 453]
10.51 - 8.9.2 Model [Seite 456]
10.52 - 8.9.3 Problems to be Solved [Seite 459]
10.53 - 8.9.4 Results and Comparison with Other Methods [Seite 459]
10.54 - 8.9.5 Concluding Remarks [Seite 461]
11 - Applications of Parallel Genetic Algorithm [Seite 462]
11.1 - 9.1 Timetabling Problem 9.1.1 Introduction [Seite 462]
11.2 - 9.1.2 Applying Genetic Algorithms to Timetabling [Seite 463]
11.3 - 9.1.3 A Parallel Algorithm [Seite 467]
11.4 - 9.1.4 Results [Seite 469]
11.5 - 9.1.5 Conclusion [Seite 470]
11.6 - 9.2 Assembling DNA Fragments with a Distributed Genetic Algorithm 9.2.1 Introduction [Seite 470]
11.7 - 9.2.2 The DNA Fragment Assembly Problem [Seite 471]
11.8 - 9.2.3 DNA Sequencing Process [Seite 472]
11.9 - 9.2.4 DNA Fragment Assembly Using the Sequential GA [Seite 474]
11.10 - 9.2.5 Implementation Details [Seite 475]
11.11 - 9.2.6 DNA Fragment Assembly Problem using the Parallel GA [Seite 477]
11.12 - 9.2.7 Experimental Results [Seite 479]
11.13 - Conclusions [Seite 485]
11.14 - 9.3 Investigating Parallel Genetic Algorithms on Job Shop Scheduling Problems 9.3.1 Introduction [Seite 486]
11.15 - 9.3.2 Job Shop Scheduling Problem [Seite 487]
11.16 - 9.3.3 Genetic Representation and Specific Operators [Seite 488]
11.17 - 9.3.4 Parallel Genetic Algorithms for JSSP [Seite 490]
11.18 - 9.3.5 Computational Results [Seite 492]
11.19 - The Effect of Parallelizing GAs [Seite 492]
11.20 - 9.3.6 Comparison of PGA Models [Seite 494]
11.21 - 9.4 Parallel Genetic Algorithm for Graph Coloring Problem 9.4.1 Introduction [Seite 496]
11.22 - Migration Model of Parallel Genetic Algorithm [Seite 496]
11.23 - 9.4.2 Genetic Operators for GCP [Seite 497]
11.24 - Sum-product Partition Crossover [Seite 497]
11.25 - 9.4.3 Experimental Verification [Seite 501]
11.26 - 9.4.4 Conclusion [Seite 503]
11.27 - 9.5 Robust and Distributed Genetic Algorithm for Ordering Problems 9.5.1 Introduction [Seite 503]
11.28 - 9.5.2 Ordering Problems [Seite 504]
11.29 - 9.5.3 Traveling Salesman Problem [Seite 505]
11.30 - 9.5.4 Distributed Genetic Algorithm [Seite 508]
11.31 - Results for Hamiltonian Cycle TSP [Seite 516]
11.32 - Results for Oliver's Hamiltonian Cycle TSP [Seite 517]
11.33 - Conclusion [Seite 519]
12 - Appendix - A Glossary [Seite 520]
12.1 - A [Seite 520]
12.2 - B [Seite 520]
12.3 - C [Seite 521]
12.4 - D [Seite 522]
12.5 - E [Seite 523]
12.6 - F [Seite 525]
12.7 - G [Seite 525]
12.8 - H [Seite 527]
12.9 - I [Seite 527]
12.10 - L [Seite 527]
12.11 - M [Seite 528]
12.12 - N [Seite 528]
12.13 - O [Seite 529]
12.14 - P [Seite 530]
12.15 - R [Seite 530]
12.16 - S [Seite 531]
12.17 - T [Seite 532]
12.18 - V [Seite 533]
13 - Appendix - B Abbreviations [Seite 534]
14 - Appendix - C Research Projects [Seite 537]
14.1 - C.1 Evolutionary Simulation-based Validation [Seite 537]
14.2 - C.2 Automatic Generation of Validation Stimuli for Application- specific Processors [Seite 537]
14.3 - C.3 Dynamic Prediction ofWeb Requests [Seite 538]
14.4 - C.4 Analog Genetic Encoding for the Evolution of Circuits and Networks [Seite 538]
14.5 - C.5 An Evolutionary Algorithm for Global Optimization Based on Level- set Evolution and Latin Squares [Seite 538]
14.6 - C.6 Imperfect Evolutionary Systems [Seite 539]
14.7 - C.7 A Runtime Analysis of Evolutionary Algorithms for Constrained Optimization Problems [Seite 539]
14.8 - C.8 Classification with Ant Colony Optimization [Seite 540]
14.9 - C.9 Multiple Choices and Reputation in Multiagent Interactions [Seite 540]
14.10 - C.10 Coarse-grained Dynamics for Generalized Recombination [Seite 541]
14.11 - C.11 An Evolutionary Algorithm-based Approach to Automated Design of Analog and RF Circuits Using Adaptive Normalized Cost Functions [Seite 541]
14.12 - C.12 An Investigation on Noisy Environments in Evolutionary Multi- objective Optimization [Seite 542]
14.13 - C.13 Interactive Evolutionary Computation-based Hearing Aid Fitting [Seite 543]
14.14 - C.14 Evolutionary Development of Hierarchical Learning Structures [Seite 543]
14.15 - C.15 Knowledge Interaction with Genetic Programming in Mechatronic Systems Design Using Bond Graphs [Seite 544]
14.16 - C.16 A Distributed Evolutionary Classifier for Knowledge Discovery in Data Mining [Seite 544]
14.17 - C.17 Evolutionary Feature Synthesis for Object Recognition [Seite 544]
14.18 - C.18 Accelerating Evolutionary Algorithms with Gaussian Process Fitness Function Models [Seite 545]
14.19 - C.19 A Constraint-based Genetic Algorithm Approach for Mining Classification Rules [Seite 545]
14.20 - C.20 An Evolutionary Algorithm for Solving Nonlinear Bilevel Programming Based on a New Constraint- handling Scheme [Seite 546]
14.21 - C.21 Evolutionary Fuzzy Neural Networks for Hybrid Financial Prediction [Seite 546]
14.22 - C.22 Genetic Recurrent Fuzzy System by Coevolutionary Computation with Divide- and- Conquer Technique [Seite 547]
14.23 - C.23 Knowledge-based Fast Evaluation for Evolutionary Learning [Seite 547]
14.24 - C.24 A Comparative Study of Three Evolutionary Algorithms Incorporating Different Amounts of Domain Knowledge for Node Covering Problem [Seite 548]
15 - Appendix - D MATLAB Toolboxes [Seite 549]
15.1 - D.1 Genetic Algorithm and Direct Search Toolbox [Seite 549]
15.2 - D.2 Genetic and Evolutionary Algorithm Toolbox [Seite 550]
15.3 - D.3 Genetic Algorithm Toolbox [Seite 551]
15.4 - D.4 Genetic Programming Toolbox for MATLAB [Seite 552]
16 - Appendix - E Commercial Software Packages [Seite 553]
16.1 - E.1 ActiveGA [Seite 553]
16.2 - E.2 EnGENEer [Seite 553]
16.3 - E.3 EvoFrame [Seite 554]
16.4 - E.4 REALizer [Seite 555]
16.5 - E.5 Evolver [Seite 555]
16.6 - E.6 FlexTool [Seite 555]
16.7 - E.7 GAME [Seite 556]
16.8 - E.8 GeneHunter [Seite 556]
16.9 - E.9 Generator [Seite 556]
16.10 - E.10 Genetic Server and Genetic Library [Seite 557]
16.11 - E.11 MicroGA [Seite 558]
16.12 - E.12 Omega [Seite 558]
16.13 - E.13 OOGA [Seite 558]
16.14 - E.14 OptiGA [Seite 559]
16.15 - E.15 PC-Beagle [Seite 559]
16.16 - E.16 XpertRule GenAsys [Seite 559]
16.17 - E.17 XYpe [Seite 559]
16.18 - E.18 Evolution Machine [Seite 560]
16.19 - E.19 Evolutionary Objects [Seite 560]
16.20 - E.20 GAC, GAL [Seite 560]
16.21 - E.21 GAGA [Seite 560]
16.22 - E.22 GAGS [Seite 561]
16.23 - E.23 GAlib [Seite 561]
16.24 - E.24 GAWorkbench [Seite 561]
16.25 - E.25 Genesis [Seite 561]
16.26 - E.26 Genie [Seite 562]
16.27 - E.27 XGenetic [Seite 562]
17 - Appendix - F GA Source Codes in 'C' Language [Seite 563]
17.1 - F.1 A "Hello World" Genetic Algorithm Example [Seite 563]
17.2 - F.2 Test Function Using sin and cos [Seite 568]
17.3 - F.3 Using Matlab to Plot Data Generated by C Language [Seite 573]
18 - Appendix - G EC Class/ Code Libraries and Software Kits [Seite 575]
18.1 - G.1 EC Class/Code Libraries [Seite 575]
18.2 - ANNEvolve [Seite 575]
18.3 - daga [Seite 576]
18.4 - dgpf [Seite 576]
18.5 - Ease [Seite 576]
18.6 - EO [Seite 576]
18.7 - FORTRAN GA [Seite 577]
18.8 - GAlib: Matthew's Genetic Algorithms Library [Seite 577]
18.9 - GALOPPS [Seite 577]
18.10 - GAS [Seite 578]
18.11 - GAUL [Seite 578]
18.12 - GECO [Seite 579]
18.13 - Genetic [Seite 579]
18.14 - GPdata [Seite 579]
18.15 - gpjpp Genetic Programming in Java [Seite 579]
18.16 - jaga [Seite 580]
18.17 - patched lil-gp [Seite 580]
18.18 - Lithos [Seite 580]
18.19 - Open BEAGLE [Seite 581]
18.20 - PGAPack [Seite 581]
18.21 - PIPE [Seite 581]
18.22 - pygene [Seite 582]
18.23 - Sugal [Seite 582]
18.24 - G.2 EC Software Kits/Applications [Seite 582]
18.25 - ADATE [Seite 582]
18.26 - esep & xesep [Seite 583]
18.27 - Corewars [Seite 583]
18.28 - Grany-3 [Seite 583]
18.29 - JCASim [Seite 584]
18.30 - JGProg [Seite 584]
19 - Bibliography [Seite 585]
