A Tutorial Survey of Genetics-based Machine Learning

This work was essentially finalised in April 2009 and contains only a handful of references to works published after that.

Abstract

This is a survey of the field of Genetics-based Machine Learning (GBML): the application of evolutionary algorithms to machine learning. We assume readers are familiar with evolutionary algorithms and their application to optimisation problems, but not necessarily with machine learning. We briefly outline the scope of machine learning, introduce the more specific area of supervised learning, contrast it with optimisation and present arguments for and against GBML. Next we introduce a framework for GBML which includes ways of classifying GBML algorithms and a discussion of the interaction between learning and evolution. We then review the following areas with emphasis on their evolutionary aspects: GBML for sub-problems of learning, genetic programming, evolving ensembles, evolving neural networks, learning classifier systems, and genetic fuzzy systems.

Links

• The publisher's version of the text will eventually be available here
• Author's preprint in PDF
• Author's preprint in HTML
• Slide version
• Bibliographies in bibtex format of (at least most of) the works cited in the survey:
  • GBML papers including evolutionary ensembles
  • Non-evolutionary ensembles papers
  • Learning Classifier Systems papers

Reference

Tim Kovacs. Genetics-based Machine Learning. To appear in Grzegorz Rozenberg, Thomas Baeck, and Joost Kok, editors, Handbook of Natural Computing: Theory, Experiments, and Applications, Springer 2011.

Bibtex

@InCollection{Kovacs2011,
  author =       {Tim Kovacs},
  title =        {Genetics-based Machine Learning},
  booktitle =    {Handbook of Natural Computing: Theory, Experiments, and Applications},
  editor =       {Grzegorz Rozenberg and Thomas B\"{a}ck and Joost Kok},
  publisher =    {Springer Verlag},
  year =         {2011},
}