Buch, Englisch, Band 20, 332 Seiten, Format (B × H): 164 mm x 241 mm, Gewicht: 667 g
Reihe: Operations Research/Computer Science Interfaces Series
A Guide to Ga Theory
Buch, Englisch, Band 20, 332 Seiten, Format (B × H): 164 mm x 241 mm, Gewicht: 667 g
Reihe: Operations Research/Computer Science Interfaces Series
ISBN: 978-1-4020-7240-6
Verlag: Springer Us
Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory is a survey of some important theoretical contributions, many of which have been proposed and developed in the Foundations of Genetic Algorithms series of workshops. However, this theoretical work is still rather fragmented, and the authors believe that it is the right time to provide the field with a systematic presentation of the current state of theory in the form of a set of theoretical perspectives. The authors do this in the interest of providing students and researchers with a balanced foundational survey of some recent research on GAs. The scope of the book includes chapter-length discussions of Basic Principles, Schema Theory, "No Free Lunch", GAs and Markov Processes, Dynamical Systems Model, Statistical Mechanics Approximations, Predicting GA Performance, Landscapes and Test Problems.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
- Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik
- Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
- Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Robotik
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
- Mathematik | Informatik Mathematik Operations Research Spieltheorie
- Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Ökonometrie
- Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Datenanalyse, Datenverarbeitung
Weitere Infos & Material
Basic Principles.- Schema Theory.- No Free Lunch for GAs.- GAs as Markov Processes.- The Dynamical Systems Model.- Statistical Mechanics Approximations.- Predicting GA Performance.- Landscapes.- Summary.
