COPYRIGHT YEAR

2012

AUTHORS

Tim Kovacs

TITLE

Genetics-Based Machine Learning

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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7 sg:copyrightHolder Springer-Verlag Berlin Heidelberg
8 sg:copyrightYear 2012
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10 sg:doi 10.1007/978-3-540-92910-9_30
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16 sg:language En
17 sg:license http://scigraph.springernature.com/explorer/license/
18 sg:metadataRights OpenAccess
19 sg:pageFirst 937
20 sg:pageLast 986
21 sg:scigraphId 4fcd4fdfea96e9e548a4bf610b5d4d3a
22 sg:title Genetics-Based Machine Learning
23 sg:webpage https://link.springer.com/10.1007/978-3-540-92910-9_30
24 rdf:type sg:BookChapter
25 rdfs:label BookChapter: Genetics-Based Machine Learning
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