Genetic Algorithms: Principles and Perspectives

A Guide to Ga Theory
2002. Auflage 2002
ISBN: 978-1-4020-7240-6
Verlag: Springer Us

Buch, Englisch, Band 20, 332 Seiten, Format (B × H): 164 mm x 241 mm, Gewicht: 667 g

Reihe: Operations Research/Computer Science Interfaces Series

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.

Reeves, Colin R

Rowe, Jonathan E

Fachgebiete

• Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Ökonometrie
• Mathematik | Informatik Mathematik Operations Research Spieltheorie
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Datenanalyse, Datenverarbeitung
• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Robotik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme

Inhaltsverzeichnis

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.