Applied Evolutionary Algorithms in JavaSpringer Science & Business Media, 2003 M04 30 - 219 páginas Genetic algorithms provide a powerful range of methods for solving complex engineering search and optimization algorithms. Their power can also lead to difficulty for new researchers and students who wish to apply such evolution-based methods. "Applied Evolutionary Algorithms in Java" offers a practical, hands-on guide to applying such algorithms to engineering and scientific problems. The concepts are illustrated through clear examples, ranging from simple to more complex problems domains; all based on real-world industrial problems. Examples are taken from image processing, fuzzy-logic control systems, mobile robots, and telecommunication network optimization problems. The Java-based toolkit provides an easy-to-use and essential visual interface, with integrated graphing and analysis tools. Topics and features: *inclusion of a complete Java toolkit for exploring evolutionary algorithms *strong use of visualization techniques, to increase understanding *coverage of all major evolutionary algorithms in common usage *broad range of industrially based example applications *includes examples and an appendix based on fuzzy logic This book is intended for students, researchers, and professionals interested in using evolutionary algorithms in their work. No mathematics beyond basic algebra and Cartesian graphs methods are required, as the aim is to encourage applying the Java toolkit to develop the power of these techniques. |
Contenido
Introduction to Evolutionary Computing | 3 |
12 History of Evolutionary Computing | 4 |
13 Obstacles to Evolutionary Computation | 5 |
15 Problem Domains | 6 |
152 Optimisation versus Robustness | 8 |
154 Fuzzy Logic | 9 |
155 Bayesian networks | 11 |
156 Artificial Neural Networks | 12 |
Future Directions in Evolutionary Computing | 103 |
63 Speciation and Distributed EA Methods | 105 |
632 Parallel Genetic Programming with Mobile Agents | 106 |
635 EA Visualisation Methods | 109 |
64 Advanced EA Techniques | 110 |
641 Multiobjective Optimisation | 111 |
644 Parameter Control | 112 |
65 Artificial Life and Coevolutionary Algorithms | 113 |
157 Feedforward Networks | 13 |
16 Applications | 14 |
161 Problems | 15 |
17 EvolutionBased Search | 16 |
173 Pascal and Fortran | 17 |
176 ObjectOriented Design | 18 |
18 Summary | 19 |
Further Reading | 20 |
Principles of Natural Evolution | 21 |
212 Biological Genes | 22 |
221 Transcription from DNA to RNA | 23 |
224 No Lamarckianism | 24 |
228 Evolvability | 25 |
2210 Sexual Recombination | 26 |
2213 Dynamics and Morphogenesis | 27 |
Further Reading | 28 |
Genetic Algorithms | 29 |
33 GA Theory | 32 |
331 Deception | 33 |
34 GA Operators | 34 |
342 Crossover | 35 |
343 Multipoint Crossover | 36 |
345 FitnessProportionate Selection | 38 |
346 Disadvantages of FitnessProportionate Selection | 39 |
348 Tournament Selection | 40 |
349 Scaling Methods | 41 |
36 Selecting GA Methods | 42 |
362 Operator Choice | 44 |
38 Summary | 47 |
Further Reading | 48 |
Genetic Programming | 49 |
421 VariableLength and TreeBased Representations | 51 |
424 Function Closure | 52 |
427 Graph Structure Encoding | 53 |
43 GP Operators | 54 |
434 Controlling Genome Growth | 55 |
44 Genetic Programming Implementation | 56 |
45 Summary | 57 |
Further Reading | 58 |
Engineering Examples Using Genetic Algorithms | 59 |
53 Basics of Image Processing | 60 |
532 Lookup Tables | 61 |
54 Java and Image Processing | 62 |
541 Example Application VEGA | 63 |
55 Spectrographic Chromosome representation | 69 |
56 Results | 70 |
57 Summary Evolved Image Processing | 72 |
58 Mobile Robot Control | 73 |
581 Artificial Intelligence and Mobile Robots | 74 |
583 Static Worlds | 75 |
584 Reactive and Bottomup Control | 76 |
585 Advantages of Reactive Control | 78 |
59 Behaviour Management | 79 |
591 Behaviour Synthesis Architecture | 80 |
592 Standard Control Methods PID | 83 |
510 Evolutionary Methods | 84 |
Natural Agents | 85 |
511 Fuzzy Logic Control | 86 |
5111 Fuzzy Control of Subsumption Architectures | 89 |
513 Robot Simulator | 91 |
5131 The Robot Control Architecture | 92 |
5132 Related Work | 95 |
5133 Results | 96 |
514 Analysis | 100 |
515 Summary Evolving Hybrid Systems | 101 |
66 Summary | 115 |
Further Reading | 116 |
The Future of Evolutionary Computing | 117 |
73 Future Directions in Evolutionary Computing | 118 |
732 Biological Inspiration | 119 |
74 Conclusion | 121 |
Bibliography | 123 |
Appendix A | 135 |
A2 CC++ based EA Software | 136 |
A4 Java Reference Guides | 138 |
Appendix B | 139 |
B21 Vectors and Arraylists | 149 |
B3 Application Design | 150 |
An Evolutionary and Ecosystem Research Platform | 151 |
B41 Introduction | 152 |
B43 Key Classes | 154 |
B44 Configuration | 155 |
B45 Illustrative Example Systems | 156 |
B47 An Ecosystem Simulation Based on Echo | 157 |
B48 Coevolutionary Function Optimisation | 158 |
B49 Telecommunications Research Using Eos | 159 |
A Telecommunications Application | 160 |
B5 Traveling Salesman Problem | 161 |
B6 Genetic Programming | 165 |
B61 Observations from Running GPsys on the Lawnmower Problem | 166 |
Eos References | 169 |
Fuzzy Logic Systems | 171 |
C2 Fuzzy Set Theory | 172 |
C21 Fuzzy Operators | 173 |
C23 Fuzzy IF | 174 |
C24 Fuzzy Associative Memories | 175 |
C25 Fuzzy Control systems | 176 |
C26 Defuzzification | 177 |
C27 Fuzzy Applications | 180 |
C31 Advantages of Fuzzy Systems | 181 |
C4 Summary | 182 |
Appendix D | 183 |
Programming Language and RunTime Environment | 184 |
Units of Measure | 185 |
NetworkLocal Connections Versus Dynamically Loaded Clients | 186 |
Why a ClientServer Architecture? | 187 |
Network and Local Connection Issues | 188 |
Communication via Events and Requests | 189 |
Keeping the RP1 Protocol LanguageIndependent | 190 |
The port and hostName Properties | 191 |
Loading RsProperties Files as a Resource | 192 |
The Server | 193 |
The Scheduler | 194 |
The FloorPlan | 196 |
Building a Virtual Robot | 198 |
Life Cycle of the Demonstration Clients | 199 |
How ClnMain Extends RsClient and Implements RsRunnable | 201 |
The RsRunnable Interface | 202 |
Building RsRunnable and RsClient into ClnMain | 203 |
DemoMain Implements RsRunnable But Does Not Extend RsClient | 204 |
Uploading the Body Plan | 206 |
Running the Event Loop | 207 |
Physical Layout of ClientZero | 209 |
RsBodyShape | 211 |
RsWheelSystem | 212 |
The Sensor Classes | 214 |
RsBodyTargetSensor | 215 |
216 | |
218 | |
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Términos y frases comunes
adaptive AddVector2Mod8 approach architecture Artificial autonomous Bayesian networks behaviour Chapter chromosome ClientZero ClnMain complex configuration control system crossover defined defuzzification double dynamically loaded client ecosystem encoding environment evaluated event handlers evolution evolutionary algorithms Evolutionary Computing evolved FIGURE fitness value fuzzy control fuzzy control system fuzzy logic fuzzy rule fuzzy sets Fuzzy Systems gene Genetic Algorithms Genetic Programming genome hardware image processing implementation index2 individual input interactions interface Java Java package Koza language machine learning mechanism method mobile robot mobile robot control mutation rate neural networks node object object-oriented optimisation output package parallel parameters phenotype pixel platform population problem domain ProgN provides reactive recombination representation scheme robot simulator rossum rossum.ini RsClient RsProperties RsRunnable search space selection sensor sequence server simple solution specific structure target task techniques Traveling Salesman Problem variable
Referencias a este libro
Evolutionäre Algorithmen: Genetische Algorithmen — Strategien und ... Ingrid Gerdes,Frank Klawonn,Rudolf Kruse Vista previa limitada - 2013 |