| Titre : | Applied evolutionary algorithms in java |
| Auteurs : | James C. Van Horne, Auteur ; Pierre Conso, Traducteur |
| Type de document : | texte imprimé |
| Année de publication : | 2003 |
| ISBN/ISSN/EAN : | 978-0-387-95568-1 |
| Format : | 219p. |
| Accompagnement : | CD Rom |
| Note générale : | Bibliogr. Index. |
| Langues : | Français |
| Index. décimale : | 025.31 (Le catalogue : forme, structure) |
| Tags : | Informatique Programmation Jjava Evolutionary programming (Computer science) Genetic algorithms Java (Computer program language) Programmation évolutionnaire Algorithmes génétiques Java (langage de programmation) |
| 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 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. |
| Note de contenu : |
Sommair:
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 |
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