An Introduction to Genetic Algorithms for Scientists and Engineers

Cover
Title page
Chapter 1 Introduction
1.1 Some Applications of Genetic Algorithms
1.2 Search Spaces
1.3 Genetic algorithms
l.4 An Examp1e
1.5 Summary
1.6 Exercies
Chapter 2 Improving the Algorithm
2.1 Comparison of Bio1ogical and GA Terminology
2.2 Robustness
2.3 Non-integer Unknowns
2.4 Multiparameter Prob1ems
2.5 Mutation
2.6 Selection
2.7 E1itism
2.8 Crossover
2.9 Initialisation
2.10 The Little Genetic algorithm
2.11 Other Evo1utionary Approaches
2.12 Summary
2.13 Exercises
Chapter 3 Foundations
3.1 Historica1 Test Functions
3.2 Schema Theory
3.3 Schema Processing
3.4 Other Theoretica1 Approaches
3.5 Summary
3.6 Exercises
Chapter 4 Advanced Operators
4.1 Combinatoria1 Optimisation
4.2 Locating Alternative Solutions Using Niches and Species
4.3 Constraints
4.4 Multicriteria Optimisation
4.5 Hybrid algorithms
4.6 Alternative Selection Methods
4.7 Alternative Crossover Methods
4.8 Considerations of Speed
4.9 Other Encodings
4.10 Meta GAs
4.11 Mutation
4.12 Parallel Genetic Algorithms
4.13 Summary
4.14 Exercises
Chapter 5 Writing a Genetic Aigorithm
Chapter 6 Applications of Genetic Aigorithms
6.1 Image Registration
6.2 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 Paper-Based Resources
Appendix B: Complete Listing of LGADOS.BAS
References
Index