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The following is from the book's contents page
Chapter 1: Introduction
1.1 Some Applications of Genetic Algorithms
1.2 Search Spaces
1.3 Genetic Algorithms
1.4 An Example
1.5 Summary
1.6 Exercises
Chapter 2: Improving the Algorithm
2.1 Comparison of Biological and GA Terminology
2.2 Robustness
2.3 Non-integer Unknowns
A Question of Accuracy
Complex Numbers
2.4 Multiparameter Problems
2.5 Mutation
2.6 Selection
2.7 Elitism
2.8 Crossover
2.9 Initialisation
2.10 The Little Genetic Algorithm
2.11 Other Evolutionary Approaches
2.12 Summary
2.13 Exercises
Chapter 3: Foundations
3.1 Historical Test Functions
Measuring Performance
The Problem of Convergence
The Application of Scaling
Genetic Drift
3.2 Schema Theory
3.3 Schema Processing
The Effect of Crossover
The Effect of Mutation
Deception
3.4 Other Theoretical Approaches
3.5 Summary
3.6 Exercises
Chapter 4: Advanced Operators
4.1 Combinatorial Optimisation
4.2 Locating Alternative Solutions Using Niches and Species
Sharing
Species
4.3 Constraints
4.4 Multicriteria Optimisation
4.5 Hybrid Algorithms
4.6 Alternative Selection Methods
Stochastic Sampling Errors
Stochastic Sampling
Ranking Methods
Tournament Selection
Sigma Scaling
Steady-State Algorithms
4.7 Alternative Crossover Methods
Two-Point Crossover
Uniform Crossover
4.8 Considerations of Speed
4.9 Other Encodings
Logarithmic Representation
Gray Encoding
4.10 Meta GAs
4.11 Mutation
4.12 Parallel Genetic Algorithms
4.13 Summary
4.14 Exercises
Chapter 5: Writing a Genetic Algorithm
Chapter 6: Applications
6.1 Image Registration
6.2 A Simple Application: Recursive Prediction of Natural Light
Levels
6.3 Water Network Design
6.4 Ground-State Energy of the ± J Spin
Glass
6.5 Estimation of the Optical Parameters of Liquid Crystals
6.6 Design of Energy Efficient Buildings
6.7 Human Judgement as the Fitness Function
6.8 Multi-objective Network Rehabilitation by Messy GA
Appendix A. Resources and other Text Books
Appendix B. Complete listing of LGADOS
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