Genetics-based Machine Learning
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:
- Tim Kovacs. Genetics-based Machine Learning. In
Grzegorz Rozenberg, Thomas Bäck, and Joost Kok, editors, Handbook of Natural Computing: Theory, Experiments, and
Applications, pages 937-986. Springer Verlag, 2012.
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.