Genetic algorithms in search, optimization, and machine learning David E Goldberg

By:

Goldberg, David E

Material type: Text

TextLanguage: English Publication details: Noida : Pearson Education India, 1998.Description: xiii, 412 pages : illustrations ; 25 cmISBN:

9788177588293
817758829X
9788177588293

Subject(s):

DDC classification:

006.31 GOL

Contents:

1. A Gentle Introduction To Genetic Algorithms --- 2. Genetic Algorithms Revisited: Mathematical Foundations --- 3. Computer Implementation Of A Genetic Algorithm --- 4. Some Applications Of Genetic Algorithms --- 5. Advanced Operators And Techniques In Genetic Search --- 6. Introduction To Genetics-Based Machine Learning --- 7. Applications Of Genetics-Based Machine Learning --- 8. A Look Back, A Glance Ahead.

Summary: This book brings together - in an informal and tutorial fashion - the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields. Major concepts are illustrated with running examples, and major algorithms are illustrated by Pascal computer programs.

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Holdings
Item type	Current library	Collection	Call number	Status	Date due	Barcode
General Books	CUTN Central Library Generalia	Non-fiction	006.31 GOL (Browse shelf(Opens below))	Checked out to M Thirugnanasambanthamoorthi (18003N)	06/05/2024	36642
General Books	CUTN Central Library Generalia	Non-fiction	006.31 GOL (Browse shelf(Opens below))	Available		36643

1. A Gentle Introduction To Genetic Algorithms ---
2. Genetic Algorithms Revisited: Mathematical Foundations ---

3. Computer Implementation Of A Genetic Algorithm ---
4. Some Applications Of Genetic Algorithms ---
5. Advanced Operators And Techniques In Genetic Search ---
6. Introduction To Genetics-Based Machine Learning ---
7. Applications Of Genetics-Based Machine Learning ---
8. A Look Back, A Glance Ahead.

This book brings together - in an informal and tutorial fashion - the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields. Major concepts are illustrated with running examples, and major algorithms are illustrated by Pascal computer programs.

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