ANN: Announcement: tutorial survey of Genetics-based Machine Learning

[Tim Kovacs]

A preprint of my tutorial survey of genetics-based machine learning
can be found here http://www.cs.bris.ac.uk/~kovacs/publications/gbml-survey/

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.

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.

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[Clif Davis]

Post by Tim Kovacs
A preprint of my tutorial survey of genetics-based machine learning
can be found herehttp://www.cs.bris.ac.uk/~kovacs/publications/gbml-survey/
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.
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.

Very nice resource. Thanks.

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