基本説明
Contains a complete overview of the field of evolutionary computing, treating all "dialects" and important algorithm variants: GAs, ES, EP, GP, LCS, MAs, MOEAs.
Full Description
The first complete overview of evolutionary computing, the collective name for a range of problem-solving techniques based on principles of biological evolution, such as natural selection and genetic inheritance. The text is aimed directly at lecturers and graduate and undergraduate students. It is also meant for those who wish to apply evolutionary computing to a particular problem or within a given application area. The book contains quick-reference information on the current state-of-the-art in a wide range of related topics, so it is of interest not just to evolutionary computing specialists but to researchers working in other fields.
Contents
1 Introduction.- 2 What is an Evolutionary Algorithm?.- 3 Genetic Algorithms.- 4 Evolution Strategies.- 5 Evolutionary Programming.- 6 Genetic Programming.- 7 Learning Classifier Systems.- 8 Parameter Control in Evolutionary Algorithms.- 9 Multimodal Problems and Spatial Distribution.- 10 Hybridisation with Other Techniques: Memetic Algorithms.- 11 Theory.- 12 Constraint Handling.- 13 Special Forms of Evolution.- 14 Working with Evolutionary Algorithms.- 15 Summary.- A Gray Coding.- B Test Functions.- References.
