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Erschienen in:
Pattern Graphs: Combining Multivariate Time Series and Labelled Interval Sequences for Classification Kapitel lesen Erstes Kapitel lesen

2013 | OriginalPaper | Buchkapitel

The Importance of Topology Evolution in NeuroEvolution: A Case Study Using Cartesian Genetic Programming of Artificial Neural Networks

verfasst von : Andrew James Turner, Julian Francis Miller

Erschienen in: Research and Development in Intelligent Systems XXX

Verlag: Springer International Publishing

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Abstract

NeuroEvolution (NE) is the application of evolutionary algorithms to Artificial Neural Networks (ANN). This paper reports on an investigation into the relative importance of weight evolution and topology evolution when training ANN using NE. This investigation used the NE technique Cartesian Genetic Programming of Artificial Neural Networks (CGPANN). The results presented show that the choice of topology has a dramatic impact on the effectiveness of NE when only evolving weights; an issue not faced when manipulating both weights and topology. This paper also presents the surprising result that topology evolution alone is far more effective when training ANN than weight evolution alone. This is a significant result as many methods which train ANN manipulate only weights.

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Vorheriges Kapitel Formulating the Temporal Causal Relationships Between Events and Their Results
Nächstes Kapitel Inferring Context from Users’ Reviews for Context Aware Recommendation
Fußnoten
1
Both CGP and ANNs can also be structured in a recurrent form.
 
2
Fully connected between layers i.e. a node in hidden layer two has an input from every node in hidden layer one.
 
3
If the arity is set high enough however all topologies are possible as each node can lower its own arity by only utilizing the first of multiple connections between two nodes.
 
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Metadaten
Titel
The Importance of Topology Evolution in NeuroEvolution: A Case Study Using Cartesian Genetic Programming of Artificial Neural Networks
verfasst von
Andrew James Turner
Julian Francis Miller
Copyright-Jahr
2013
Buch
Research and Development in Intelligent Systems XXX

Print ISBN: 978-3-319-02620-6

Electronic ISBN: 978-3-319-02621-3

Copyright-Jahr: 2013

DOI
https://doi.org/10.1007/978-3-319-02621-3_15

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