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Practical Genetic Algorithms by Sue Ellen Haupt and Randy L. Haupt HC 2004
US $11.00
ApproximatelyRM 47.24
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A book in excellent condition. Cover is shiny and undamaged, and the dust jacket is included for hard covers. No missing or damaged pages, no creases or tears, and no underlining/highlighting of text or writing in the margins. May be very minimal identifying marks on the inside cover. Very minimal wear and tear.
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Item specifics
- Condition
- ISBN
- 9780471455653
About this product
Product Identifiers
Publisher
Wiley & Sons, Incorporated, John
ISBN-10
0471455652
ISBN-13
9780471455653
eBay Product ID (ePID)
30477828
Product Key Features
Number of Pages
288 Pages
Language
English
Publication Name
Practical Genetic Algorithms
Publication Year
2004
Subject
Programming / Algorithms, Electrical
Features
Revised
Type
Textbook
Subject Area
Computers, Technology & Engineering
Format
Hardcover
Dimensions
Item Height
0.6 in
Item Weight
18.3 Oz
Item Length
9.5 in
Item Width
6.3 in
Additional Product Features
Edition Number
2
Intended Audience
Scholarly & Professional
LCCN
2004-043360
Dewey Edition
22
Reviews
"Statisticians and computing scientists will like this book very much and will benefit greatly from it." ( Journal of Statistical Computation and Simulation , November 2005) "...an excellent introduction to the world of optimization with its distinct vocabulary and tools." ( Journal of the American Statistical Association , September 2005) "I recommend it highly to anyone who is interested in trying to explore this powerful tool to optimization problems in his or her area of interest." ( International Journal of General Systems , June 2005) "...a nice step-by-step introduction to genetic algorithms (GA) which is specifically designed for practitioners..." ( Journal of Intelligent & Fuzzy Systems , Vol. 16, No. 2, 2005) "This book is very nice to read. It is ideal for some interesting evening study." ( Technometrics , May 2005) "...this book is a worthwhile addition to any course in optimization and/or Gas. It could also serve as a practical guide and template source for researchers..." ( Computing Reviews.com , September 30, 2004)
Illustrated
Yes
Dewey Decimal
519.62
Table Of Content
Preface. Preface to First Edition. List of Symbols. 1. Introduction to Optimization. 1.1 Finding the Best Solution. 1.2 Minimum-Seeking Algorithms. 1.3 Natural Optimization Methods. 1.4 Biological Optimization: Natural Selection. 1.5 The Genetic Algorithm. 2. The Binary Genetic Algorithm. 2.1 Genetic Algorithms: Natural Selection on a Computer. 2.2 Components of a Binary Genetic Algorithm. 2.3 A Parting Look. 3. The Continuous Genetic Algorithm. 3.1 Components of a Continuous Genetic Algorithm. 3.2 A Parting Look. 4. Basic Applications. 4.1 "Mary Had a Little Lamb". 4.2 Algorithmic Creativity-Genetic Art. 4.3 Word Guess. 4.4 Locating an Emergency Response Unit. 4.5 Antenna Array Design. 4.6 The Evolution of Horses. 4.7 Summary. 5. An Added Level of Sophistication. 5.1 Handling Expensive Cost Functions. 5.2 Multiple Objective Optimization. 5.3 Hybrid GA. 5.4 Gray Codes. 5.5 Gene Size. 5.6 Convergence. 5.7 Alternative Crossovers for Binary GAs. 5.8 Population. 5.9 Mutation. 5.10 Permutation Problems. 5.11 Selling GA Parameters. 5.12 Continuous versus Binary GA. 5.13 Messy Genetic Algorithms. 5.14 Parallel Genetic Algorithms. 6. Advanced Applications. 6.1 Traveling Salespersons Problem. 6.2 Locating an Emergency Response Unit Revisited. 6.3 Decoding a Secret Message. 6.4 Robot Trajectory Planning. 6.5 Stealth Design. 6.6 Building Dynamical Inverse Models-The Linear Case. 6.7 Building Dynamical Inverse Models-The Nonlinear Case. 6.8 Combining GAs with Simulations-Air Pollution Receptor Modeling. 6.9 Combining Methods Neural Nets with GAs. 6.10 Solving High-Order Nonlinear Partial Differential Equations. 7. More Natural Optimization Algorithms. 7.1 Simulated Annealing. 7.2 Particle Swarm Optimization (PSO). 7.3 Ant Colony Optimization (ACO). 7.4 Genetic Programming (GP). 7.5 Cultural Algorithms. 7.6 Evolutionary Strategies. 7.7 The Future of Genetic Algorithms. Appendix I: Test Functions. Appendix II: MATLAB Code. Appendix III. High-Performance Fortran Code. Glossary. Index.
Edition Description
Revised edition
Synopsis
This much anticipated second edition deals with the fundamentals of genetic algorithms and their applications in a variety of different areas of engineering and science. The clear presentation and comprehensive nature of the first edition is continued in this book to make it an excellent introduction to the subject of gentic algorithms., * This book deals with the fundamentals of genetic algorithms and their applications in a variety of different areas of engineering and science * Most significant update to the second edition is the MATLAB codes that accompany the text * Provides a thorough discussion of hybrid genetic algorithms * Features more examples than first edition, "The first introductory-level book to emphasize practical applications through the use of example problems." International Journal of General Systems, Vol. 31, No. 1, 2002, on the first edition The use of genetic algorithms (GAs) to solve large and often complex computational problems has given rise to many new applications in a variety of disciplines. Practical Genetic Algorithms was the first introductory-level book on genetic algorithms to emphasize practical applications rather than theory. Practical Genetic Algorithms, Second Edition reflects the significant evolution of the field since the book's first edition. In an accessible style, the authors explain why the genetic algorithm is superior in many real-world applications, cover continuous parameter genetic algorithms, and provide in-depth trade-off analysis of genetic algorithm parameter selection. This Second Edition features: Numerous practical example problems A CD-ROM with MATLAB and High Performance Fortran codes A new, more complete picture of traditional optimization Revised examples reflecting recent research Coverage of pareto-genetic and hybrid genetic algorithms (GAs) New sections on hybrid GAs, parallel GAs, and messy GAs, with recommendations on improving their performance An all new chapter on simulated annealing, ant-colony optimization, evolutionary strategies, and other cutting-edge artificial intelligence methods of optimization Written for the practicing scientist, engineer, economist, artist, or anyone with an interest in the basics of GAs, the second edition continues to offer readers an up-to-date look at the evolving practical applications of GAs and how to manipulate them in order to get the best performance.
LC Classification Number
QA402.5.H387 2004
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