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

Wei Pang, George MacLeod Coghill

Research output: Contribution to conference › Paper › peer-review

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
Pages	43-48
Number of pages	6
Publication status	Published - 2011
Event	11th UK Workshop on Computational Intelligence 2011 - Manchester, United Kingdom Duration: 7 Sept 2011 → 9 Sept 2011

Conference

Conference	11th UK Workshop on Computational Intelligence 2011
Abbreviated title	UKCI 2011
Country/Territory	United Kingdom
City	Manchester
Period	7/09/11 → 9/09/11

Cite this

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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}",

year = "2011",

language = "English",

pages = "43--48",

note = "11th UK Workshop on Computational Intelligence 2011, UKCI 2011 ; Conference date: 07-09-2011 Through 09-09-2011",

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AU - Pang, Wei

AU - Coghill, George MacLeod

PY - 2011

Y1 - 2011

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.

M3 - Paper

SP - 43

EP - 48

T2 - 11th UK Workshop on Computational Intelligence 2011

Y2 - 7 September 2011 through 9 September 2011

ER -

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

Abstract

Conference

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