Genetic Algorithms: Principles and Perspectives : A Guide to Ga Theory

Colin R Reeves

ISBN 10: 1402072406 / ISBN 13: 9781402072406

Published by Springer Us Dez 2002, 2002

About this Item

Neuware - 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.

Seller Inventory # 9781402072406

Bibliographic Details

• Title: Genetic Algorithms: Principles and Perspectives : A Guide to Ga Theory
• Publisher: Springer Us Dez 2002
• Publication Date: 2002
• Language: English
• ISBN 10: 1402072406
• ISBN 13: 9781402072406
• Binding: Buch
• Condition: Neu

About this title

Synopsis

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.