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Genetics-based Machine Learning

Tim Kovacs

Department of Computer Science
University of Bristol

This is the author's final version (dated April 2009) of a chapter which appeared in Springer Verlag's Handbook of Natural Computing in 2012. The published version is available from By default please cite the published version:


In the author's final version (i.e. what you are reading now), section 3.5 Learning Classifier Systems has a new subsubsection 3.5.2 Representation and several other subsubsections from the published version were moved into the new one. This version also has a more detailed table of contents than the published version.


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.

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T Kovacs 2011-03-12