Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory
Author:   Colin R. Reeves ,  Jonathan E. Rowe
Publisher:   Springer-Verlag New York Inc.
Edition:   Softcover reprint of the original 1st ed. 2002
Volume:   20
ISBN:  

9781475778182


Pages:   332
Publication Date:   22 May 2013
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory


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Overview

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.

Full Product Details

Author:   Colin R. Reeves ,  Jonathan E. Rowe
Publisher:   Springer-Verlag New York Inc.
Imprint:   Springer-Verlag New York Inc.
Edition:   Softcover reprint of the original 1st ed. 2002
Volume:   20
Dimensions:   Width: 15.50cm , Height: 1.80cm , Length: 23.50cm
Weight:   0.534kg
ISBN:  

9781475778182


ISBN 10:   147577818
Pages:   332
Publication Date:   22 May 2013
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of Contents

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.

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