Table of Contents: Genetic algorithms and evolution strategy in engineering and computer science:

Corporate Authors:	John Wiley and Sons, Wiley
Other Authors:	QUAGLIARELLA, D., PERIAUX, J., POLONI, C., WINTER, GEORGE, Quagliarella, D.
Format:	Book
Language:	English
Published:	Chichester: John Wiley, 1998
Edition:	2nd ed.
Subjects:	ΓΕΤΕ ENGINEERING COMPUTER SCIENCE EVOLUTION STRATEGY Algorithm ELECTRICAL ENGINEERING ELECTROMAGNETIC MANUFACTURING Μηχανική Πληροφορική Ηλεκτρομαγνητική Ηλεκτρολογία > Μηχανολογία Εξελικτική στρατηγική Αλγόριθμοι Βιομηχανικές εφαρμογές Aerodynamics Αεροδυναμική Genetic algorithms > Data processing Evolutionary programming (Computer science) Evolutionary computation Engineering Genetic algorithms. Γενετικοί αλγόριθμοι Genetic algorithms Engineering > Data processing

Table of Contents:

1. Artificial evolution: how and why?
2. Adaptive niching via coevolutionary sharing
3. Representation issues in neighbourhood search and evolutionary algorithms
4. Gene expression: the missing link in evolutionary computation
5. Immunized artificial systems - concepts and applications
6. Designing electronic circuits using evolutionary algorithms arithmetic circuits: a case study
7. Evolutionary computing for conceptual and detailed design
8. Cam shape optimization by genetic algorithms
9. Evolutionary algorithms: applications at the Informatik Center Dortmund
10. Evolutionary learning processes for data analysis in electrical engineering applications
11. Ga multiple objective optimization strategies for electromagnetic backscattering
12. Pareto genetic algorithm for aerodynamic design using the Navier-Stokes equations
13. GA coupled with computationally expensive simulations: tools to improve efficiency
14. Coupling genetic algorithms and gradient based optimization techniques
15. Evolutionary synthesis of control policies for manufacturing systems
16. Parametric and non-parametric identification of macro-mechanical models
17. Evolutionary mobile robotics
18. Nonlinear system identification by means of evolutionary optimised neural networks.