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

Research output: Chapter in Book/Report/Conference proceedingPublished 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 languageEnglish
Title of host publicationProceedings of the 11th UK Workshop on Computational Intelligence (UKCI)
Place of PublicationUniversity of Manchester
PublisherUniversity of Manchester
Pages43-48
Number of pages6
Publication statusPublished - Sept 2011
Event11th UK Workshop on Computational Intelligence - Manchester, United Kingdom
Duration: 7 Sept 20119 Sept 2011

Conference

Conference11th UK Workshop on Computational Intelligence
Country/TerritoryUnited Kingdom
CityManchester
Period7/09/119/09/11

Bibliographical 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.

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