Résumé
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.
Contents
- Introduction to evolutionary computing
- Principles natural evolution
- Genetic algorithms
- Genetic programming
- Engineering examples using genetic algorithms
- Future directions in evolutionary computing
- The future of evolutionary computing
Appendixes
Index
L'auteur - Robert Ghanea-Hercock
Intelligent Systems Laboratory, Ipswich, UK
Caractéristiques techniques
| PAPIER | |
| Éditeur(s) | Springer |
| Auteur(s) | Robert Ghanea-Hercock |
| Parution | 15/05/2003 |
| Nb. de pages | 232 |
| Format | 16 x 24 |
| Couverture | Relié |
| Poids | 485g |
| Intérieur | Noir et Blanc |
| EAN13 | 9780387955681 |
| ISBN13 | 978-0-387-95568-1 |
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