Contents

• 1. Introduction
  • 1.1 Machine Learning
  • 1.2 Arguments For and Against GBML
  
  
• 2. A Framework for GBML
  • 2.1 Classifying GBML Systems by Role
  • 2.2 Classifying GBML Systems Algorithmically
  • 2.3 The Interaction of Learning and Evolution
  • 2.4 Other GBML Models
  
  
• 3. GBML Areas
  • 3.1 GBML for Sub-problems of Learning
  • 3.2 Genetic Programming
    • 3.2.1 GP Trees
    • 3.2.2 Decision Trees
    • 3.2.3 Extensions to GP
    • 3.2.4 Conclusions
  • 3.3 Evolving Ensembles
    • 3.3.1 Evolutionary Ensembles
    • 3.3.2 Conclusions
  • 3.4 Evolving Neural Networks
    • 3.4.1 Ensembles of NNs
    • 3.4.2 Yao's Framework for Evolving NNs
    • 3.4.3 Conclusions
  • 3.5 Learning Classifier Systems
    • 3.5.1 Production Systems and Rule(Set) Parameters
    • 3.5.2 Representation
    • 3.5.3 Rule Discovery
    • 3.5.4 LCS Credit Assignment
    • 3.5.5 Conclusions
  • 3.6 Genetic Fuzzy Systems
    • 3.6.1 Evolution of FRBSs
    • 3.6.2 Genetic Neuro-fuzzy Systems
    • 3.6.3 Conclusions
  
  
• 4. Conclusions

• Acknowledgements
• Glossary
• Bibliography
• About this document ...