Genetic Algorithms in Engineering and Computer Science
John Wiley & Sons Ltd (Verlag)
978-0-471-97710-0 (ISBN)
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Genetic Algorithms (GA) and Evolution Strategies (ES) are stochastic-based search techniques for solving previously insurmountable optimization problems on computers. This book seeks to present a compendium of lectures delivered by specialists in the field on theoretical, numerical and applied aspects of GA for the computational treatment of continuous, discrete and combinatorial optimization problems. These lectures will be given at the second EUROGEN course, held in Trieste, Italy, in December 1997.
Artificial Evolution: How and Why?; (H.-P; Schwefel & T. Bdck); Adaptive Niching via Coevolutionary Sharing (D. Goldberg & L. Wang); Representation Issues in Neighbourhood Search and Evolutionary Algorithms (D. Whitley, et al.); Gene Expression: The Missing Link in Evolutionary Computation (H. Kargupta); Immunized Artificial Systems Concepts and Applications (K. Krishnakumar & J. Neidhoefer); Designing Electronic Circuits Using Eolutionary Algorithms Arithmetic Circuits: A Case Study (J. Miller, et al.); Evolutionary Computing for Conceptual and Detailed Design (I. Parmee); Cam Shape Optimization by Genetic Algorithms (J. Alander & J. Lampinen); Evolutionary Algorithms: Applications at the Informatik Center Dortmund (T. Bdck, et al.); Evolutionary Learning Processes for Data Analysis in Electrical Engineering Applications (O. Cordsn, et al.); Ga Multiple Objective Optimization Strategies for Electromagnetic Backscattering (J. Piriaux, et al.); Pareto Genetic Algorithm for Aerodynamic Design Using the Navier-Stokes Equations (S. Obayashi); GA Coupled with Computationally Expensive Simulations: Tools to Improve Efficiency (G. Poloni & V. Pediroda); Coupling Genetic Algorithms and Gradient Based Optimization Techniques (D. Quagliarella & A. Vicini); Evolutionary Synthesis of Control Policies for Manufacturing Systems (B. Porter); Parametric and Non-Parametric Identification of Macro-Mechanical Models (M. Sebag, et al.); Evolutionary Mobile Robotics (D. Floreano); Nonlinear System Identification by Means of Evolutionary Optimised Neural Networks (I. De Falco).
Erscheint lt. Verlag | 26.11.1997 |
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Zusatzinfo | Illustrations |
Verlagsort | Chichester |
Sprache | englisch |
Maße | 172 x 250 mm |
Gewicht | 820 g |
Einbandart | gebunden |
Themenwelt | Informatik ► Grafik / Design ► Digitale Bildverarbeitung |
Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
Mathematik / Informatik ► Mathematik ► Angewandte Mathematik | |
Technik ► Maschinenbau | |
ISBN-10 | 0-471-97710-1 / 0471977101 |
ISBN-13 | 978-0-471-97710-0 / 9780471977100 |
Zustand | Neuware |
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