A fast opt-AINet approach to qualitative model learning with a modified mutation operator.

Wei Pang; George MacLeod Coghill

A fast opt-AINet approach to qualitative model learning with a modified mutation operator.

Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

Abstract

This research furthers our previous work on developing QML-AINet, a learning system that applies OptAINet, an immune network approach to optimisation problems, to the field of Qualitative Model Learning (QML). The mutation operator of Opt-AINet was modified to improve the efficiency of QML-AINet, and experiments showed that for dealing with QML, QML-AINet with the newly modified mutation operator outperformed both previous systems: QML-CLONALG and the early version of QML-AINet.

Original language	English
Title of host publication	Proceedings of the 11th UK Workshop on Computational Intelligence (UKCI)
Place of Publication	University of Manchester
Pages	43-48
Number of pages	6
Publication status	Published - Sep 2011
Event	11th UK Workshop on Computational Intelligence - Manchester, United Kingdom Duration: 7 Sep 2011 → 9 Sep 2011

Conference

Conference	11th UK Workshop on Computational Intelligence
Country/Territory	United Kingdom
City	Manchester
Period	7/09/11 → 9/09/11

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http://ukci.cs.manchester.ac.uk/files/Proceedings.pdfLicence: Unspecified

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Pang, W & Coghill, GM 2011, A fast opt-AINet approach to qualitative model learning with a modified mutation operator. in Proceedings of the 11th UK Workshop on Computational Intelligence (UKCI). University of Manchester, pp. 43-48, 11th UK Workshop on Computational Intelligence, Manchester, United Kingdom, 7/09/11. <http://ukci.cs.manchester.ac.uk/files/Proceedings.pdf>

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title = "A fast opt-AINet approach to qualitative model learning with a modified mutation operator.",

abstract = "This research furthers our previous work on developing QML-AINet, a learning system that applies OptAINet, an immune network approach to optimisation problems, to the field of Qualitative Model Learning (QML). The mutation operator of Opt-AINet was modified to improve the efficiency of QML-AINet, and experiments showed that for dealing with QML, QML-AINet with the newly modified mutation operator outperformed both previous systems: QML-CLONALG and the early version of QML-AINet.",

author = "Wei Pang and Coghill, {George MacLeod}",

note = "WP and GMC are supported by the CRISP project (Combinatorial Responses In Stress Pathways) funded by the BBSRC (BB/F00513X/1) under the Systems Approaches to Biological Research (SABR) Initiative. WP acknowledges the support of the 2011 Researcher International Networking grant from the British Council. ; 11th UK Workshop on Computational Intelligence ; Conference date: 07-09-2011 Through 09-09-2011",

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AU - Coghill, George MacLeod

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PY - 2011/9

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N2 - This research furthers our previous work on developing QML-AINet, a learning system that applies OptAINet, an immune network approach to optimisation problems, to the field of Qualitative Model Learning (QML). The mutation operator of Opt-AINet was modified to improve the efficiency of QML-AINet, and experiments showed that for dealing with QML, QML-AINet with the newly modified mutation operator outperformed both previous systems: QML-CLONALG and the early version of QML-AINet.

AB - This research furthers our previous work on developing QML-AINet, a learning system that applies OptAINet, an immune network approach to optimisation problems, to the field of Qualitative Model Learning (QML). The mutation operator of Opt-AINet was modified to improve the efficiency of QML-AINet, and experiments showed that for dealing with QML, QML-AINet with the newly modified mutation operator outperformed both previous systems: QML-CLONALG and the early version of QML-AINet.

UR - http://ukci.cs.manchester.ac.uk/index.html

M3 - Published conference contribution

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EP - 48

BT - Proceedings of the 11th UK Workshop on Computational Intelligence (UKCI)

CY - University of Manchester

T2 - 11th UK Workshop on Computational Intelligence

Y2 - 7 September 2011 through 9 September 2011

ER -

A fast opt-AINet approach to qualitative model learning with a modified mutation operator.

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