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.
|Title of host publication||Proceedings of the 11th UK Workshop on Computational Intelligence (UKCI)|
|Place of Publication||University of Manchester|
|Publisher||University of Manchester|
|Number of pages||6|
|Publication status||Published - Sept 2011|
|Event||11th UK Workshop on Computational Intelligence - Manchester, United Kingdom|
Duration: 7 Sept 2011 → 9 Sept 2011
|Conference||11th UK Workshop on Computational Intelligence|
|Period||7/09/11 → 9/09/11|
Bibliographical noteWP 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.