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Practical Genetic Algorithms [Kõva köide]

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  • Formaat: Hardback, 192 pages, kõrgus x laius: 240x160 mm, kaal: 454 g, Illustrations
  • Ilmumisaeg: 05-Jan-1998
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 0471188735
  • ISBN-13: 9780471188735
Teised raamatud teemal:
Practical Genetic AlgorithmsSuurem pilt
  • Formaat: Hardback, 192 pages, kõrgus x laius: 240x160 mm, kaal: 454 g, Illustrations
  • Ilmumisaeg: 05-Jan-1998
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 0471188735
  • ISBN-13: 9780471188735
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780471188735.html
Märksõnad:
Per the glossary of this introductory text, a genetic algorithm (GA) is a type of computation that models the biological genetic process by including crossover and mutation operators. R. Haupt (electrical engineering, U. of Nevada, Reno) and S. Haupt (atmospheric and oceanic science, U. of Colorado, Boulder) explain GA parameters, applications, and trends in computer modeling of natural processes. Includes a list of symbols, and rather than divorcing over which computer language codes to provide pseudocodes for customizing GAs. Annotation c. by Book News, Inc., Portland, Or.

A tutorial on genetic algorithms with an emphasis on practical applications

The rapidly expanding field of genetic algorithms has given rise to many new applications in a variety of disciplines. However, most of the existing books on the subject concentrate on theory. Practical Genetic Algorithms is the first introductory-level book to emphasize practical applications through the use of example problems.

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. Written for the end user in engineering, science, and computer programming, as well as upper-level undergraduate and graduate students, Practical Genetic Algorithms:
* Provides numerous practical example problems
* Contains over 80 illustrations
* Features many figures and tables
* Includes three appendices: a glossary of terms, a list of genetic algorithm routines in pseudocode, and a list of symbols used in the book.

Arvustused

"...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)

PREFACE xi(2)
LIST OF SYMBOLS
xiii
1 INTRODUCTION TO OPTIMIZATION
1(24)
1.1 Finding the Best Solution
2(4)
1.1.1 What is Optimization?
2(1)
1.1.2 Root Finding vs. Optimization
3(1)
1.1.3 Categories of Optimization
4(2)
1.2 Minimum Seeking Algorithms
6(10)
1.2.1 Exhaustive Search
6(2)
1.2.2 Analytical Optimization
8(3)
1.2.3 Nelder-Mead Downhill Simplex Method
11(1)
1.2.4 Optimization Based on Line Minimization
12(4)
1.3 Natural Optimization Methods
16(2)
1.3.1 Simulated Annealing
16(1)
1.3.2 The Genetic Algorithm
17(1)
1.4 Biological Optimization: Natural Selection
18(5)
Bibliography
23(2)
2 THE BINARY GENETIC ALGORITHM
25(24)
2.1 Genetic Algorithms: Natural Selection on a Computer
25(2)
2.2 Components of a Binary Genetic Algorithm
27(21)
2.2.1 Selecting the Parameters and the Cost Function
28(4)
2.2.2 Parameter Representation
32(2)
2.2.3 Initial Population
34(2)
2.2.4 Natural Selection
36(2)
2.2.5 Pairing
38(3)
2.2.6 Mating
41(1)
2.2.7 Mutations
41(2)
2.2.8 The Next Generation
43(3)
2.2.9 Convergence
46(2)
2.3 A Parting Look
48(1)
Bibliography
48(1)
3 THE CONTINUOUS PARAMETER GENETIC ALGORITHM
49(17)
3.1 Components of a Continuous Parameter Genetic Algorithm
50(12)
3.1.1 The Example Parameters and Cost Function
51(1)
3.1.2 Parameter Encoding, Accuracy, and Bounds
52(1)
3.1.3 Initial Population
52(2)
3.1.4 Natural Selection
54(1)
3.1.5 Pairing
54(2)
3.1.6 Mating
56(4)
3.1.7 Mutations
60(2)
3.1.8 Convergence
62(1)
3.2 A Parting Look
62(2)
Bibliography
64(2)
4 APPLICATIONS
66(19)
4.1 "Mary Had a Little Lamb"
66(4)
4.2 Word Guess
70(4)
4.3 Locating an Emergency Response Unit
74(3)
4.4 Antenna Array Design
77(6)
4.5 Summary
83(1)
Bibliography
84(1)
5 AN ADDED LEVEL OF SOPHISTICATION
85(34)
5.1 Handling Expensive Cost Functions
85(3)
5.2 Gray Codes
88(3)
5.3 Gene Size
91(1)
5.4 Population
91(11)
5.5 Convergence
102(2)
5.6 Alternative Crossovers for Binary Genetic Algorithms
104(2)
5.7 Mutation
106(2)
5.8 Permutation Problems
108(5)
5.9 Selecting Genetic Algorithm Parameters
113(3)
5.10 Continuous vs. Binary Genetic Algorithm
116(1)
Bibliography
116(3)
6 ADVANCED APPLICATIONS
119(27)
6.1 Traveling Salesman
120(3)
6.2 Locating an Emergency Response Unit Revisited
123(1)
6.3 Decoding a Secret Message
123(3)
6.4 Robot Trajectory Planning
126(5)
6.5 Stealth Design
131(5)
6.6 Building a Dynamical Inverse Model
136(4)
6.7 Solving High-Order Nonlinear Partial Differential Equations
140(3)
Bibliography
143(3)
7 EVOLUTIONARY TRENDS
146(13)
7.1 The Past
146(1)
7.2 The Present
147(7)
7.2.1 Other Research Areas
147(6)
7.2.2 Reference Materials
153(1)
7.3 The Future
154(1)
Bibliography
155(4)
APPENDIX A PSEUDOCODES 159(8)
GLOSSARY 167(8)
INDEX 175
RANDY L. HAUPT, PhD, is Professor and Chair of Electrical Engineering at the University of Nevada, Reno. SUE ELLEN HAUPT, PhD, is a research associate in the Program in Atmospheric and Oceanic Science at the University of Colorado, Boulder.