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Produktbild: Practical Genetic Algorithms

Practical Genetic Algorithms

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Beschreibung

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...

Beschreibung

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

Produktdetails

Einband

Taschenbuch
Erscheinungsdatum

31.05.2004
Verlag John Wiley & Sons Inc
Seitenzahl

288

Produktdetails

Einband

Taschenbuch
Erscheinungsdatum

31.05.2004
Verlag John Wiley & Sons Inc
Seitenzahl

288
Maße (L/B/H)

24,2/15,4/1,9 cm
Gewicht

531 g
Auflage

2nd edition
Sprache

Englisch
ISBN

978-0-471-45565-3
Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: gpsr@libri.de

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Die Leseprobe wird geladen.
  • Produktbild: Practical Genetic Algorithms
  • 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.