Practical Genetic Algorithms
-15%
portes grátis
Practical Genetic Algorithms
Haupt, Sue Ellen; Haupt, Randy L.
John Wiley & Sons Inc
06/2004
288
Dura
Inglês
9780471455653
15 a 20 dias
492
Descrição não disponível.
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.
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.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
practical; emphasize; example; first; book; problems; introductorylevel; use; applications; international journal; large; algorithms; computational; genetic; complex; gas
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.
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.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
