Applied Evolutionary Algorithms in JavaSpringer Science & Business Media, 2013 M03 20 - 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
1 | |
Principles of Natural Evolution | 19 |
Genetic Algorithms | 27 |
Genetic Programming | 47 |
Engineering Examples Using Genetic Algorithms | 57 |
Future Directions in Evolutionary Computing | 101 |
The Future of Evolutionary Computing | 115 |
Bibliography 121 | 120 |
Appendix A | 133 |
Appendix C | 169 |
Appendix D | 181 |
Physical Layout of ClientZero | 207 |
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Términos y frases comunes
adaptive AddVector2Mod8 application approach architecture Artificial autonomous robot Bayesian networks behaviour Chapter chromosome ClientZero ClnMain complex configuration control system crossover defined defuzzification double ecosystem encoding environment evaluated event handlers evolution evolutionary algorithms Evolutionary Computation evolved example FIGURE fitness value fuzzy control fuzzy control system fuzzy logic fuzzy rule fuzzy sets fuzzy system gene Genetic Algorithms Genetic Programming genome Goldberg hardware image processing implementation index2 input interactions interface Java Java package Koza language machine learning mechanism method mobile robot control mutation rate Neural Networks node object object-oriented optimisation output package parallel parameters phenotype pixel platform population Proc ProgN provides reactive real-world recombination representation scheme robot simulator rossum rossum.ini RP1 simulator RsClient RsProperties RsRunnable search space selection sensor sequence server simple solution specific structure target task techniques variable