Genetic Algorithms: Principles and Perspectives

Name: Genetic Algorithms: Principles and Perspectives
Brand: Springer-Verlag New York Inc.
SKU: 9781402072406
Price: 63361 HUF
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A Guide to GA Theory

Szerző: Colin R. Reeves, Jonathan E. Rowe

Nyelv:

Kötés: Kemény kötésű

Kiadó: Springer-Verlag New York Inc.

Elérhetőség: Beszállítói készleten

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Genetic Algorithms (GAs) have become a highly effective tool for solving hard optimization problems....

Információk a könyvről

Szerző

Colin R. Reeves, Jonathan E. Rowe

Nyelv

Angol

Kötés

Könyv - Kemény kötésű

Kiadva

2002

oldal

332

EAN

9781402072406

ISBN

1402072406

Enbook ID

05256339

Kiadó

Springer-Verlag New York Inc.

Súly

710

Méretek

155 x 235 x 25

Teljes leírás

Genetic Algorithms (GAs) have become a highly effective tool for solving hard optimization problems. As their popularity has increased, the number of GA applications has grown in more than equal measure. Genetic Algorithm theory, however, has not kept pace with the growing use and application of GAs. Most book-length treatments of GAs provide only a cursory discussion of theory and this discussion primarily focuses on the traditional view, which depends heavily on the concept of a "schema". 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. §The authors have worked hard to make the book as accessible as possible for students and researchers. An undergraduate-level mathematical understanding of linear algebra and stochastic processes is assumed. For those readers who have not encountered GAs before, a comprehensive survey of GA concepts is provided and the variety of ways in which GAs can be implemented is outlined. Exercises are provided at the ends of the chapters with the express purpose of aiding understanding of the concepts discussed and to whet the reader's appetite for pursuing theoretical research in GAs.

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Genetic Algorithms: Principles and Perspectives

A Guide to GA Theory

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