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Genetics-Based Machine Learning

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Handbook of Natural Computing

Abstract

This is a survey of the field of genetics-based machine learning (GBML): the application of evolutionary algorithms (ES) to machine learning. We assume readers are familiar with evolutionary algorithms and their application to optimization 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 optimization 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 subproblems of learning, genetic programming, evolving ensembles, evolving neural networks, learning classifier systems, and genetic fuzzy systems.

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Acknowledgments

Thanks to my editor Thomas Bäck for his patience and encouragement, and to Larry Bull, John R. Woodward, Natalio Krasnogor, Gavin Brown and Arjun Chandra for comments.

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Authors and Affiliations

  1. Department of Computer Science, University of Bristol, Bristol, UK

    Tim Kovacs

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  1. Tim Kovacs
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Correspondence to Tim Kovacs .

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Editors and Affiliations

  1. LIACS, Leiden University, Leiden, The Netherlands

    Grzegorz Rozenberg

  2. Computer Science Department, University of Colorado, Boulder, USA

    Grzegorz Rozenberg

  3. LIACS, Leiden University, Leiden, The Netherlands

    Thomas Bäck

  4. LIACS, Leiden University, Leiden, The Netherlands

    Joost N. Kok

Glossary

EA

Evolutionary Algorithm

FRBS

Fuzzy Rule-Based System

GA

Genetic Algorithm

GBML

Genetics-Based Machine Learning

GFS

Genetic Fuzzy System

GP

Genetic Programming

LCS

Learning Classifier System

NN

Neural Network

SL

Supervised Learning

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Kovacs, T. (2012). Genetics-Based Machine Learning. In: Rozenberg, G., Bäck, T., Kok, J.N. (eds) Handbook of Natural Computing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92910-9_30

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