Abstract
| Original language | English |
|---|---|
| Pages (from-to) | 148-157 |
| Number of pages | 10 |
| Journal | Applied Soft Computing |
| Volume | 27 |
| Early online date | 20 Nov 2014 |
| DOIs | |
| Publication status | Published - Feb 2015 |
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Keywords
- qualitative model learning
- artificial immune systems
- immune network approach
- compartmental models
- qualitative reasoning
- qualitative differential equation
Cite this
QML-AiNet : an immune network approach to learning qualitative differential equation models. / Pang, Wei; Coghill, George M.
In: Applied Soft Computing, Vol. 27, 02.2015, p. 148-157.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - QML-AiNet
T2 - an immune network approach to learning qualitative differential equation models
AU - Pang, Wei
AU - Coghill, George M
N1 - Acknowledgements WP and GMC are supported by the CRISP project (Combinatorial Responses in Stress Pathways) funded by the BBSRC (award reference: BB/F00513X/1) under the Systems Approaches to Biological Research (SABR) Initiative.
PY - 2015/2
Y1 - 2015/2
N2 - In this paper, we explore the application of Opt-AiNet, an immune network approach for search and optimisation problems, to learning qualitative models in the form of qualitative differential equations. The Opt-AiNet algorithm is adapted to qualitative model learning problems, resulting in the proposed system QML-AiNet. The potential of QML-AiNet to address the scalability and multimodal search space issues of qualitative model learning has been investigated. More importantly, to further improve the efficiency of QML-AiNet, we also modify the mutation operator according to the features of discrete qualitative model space. Experimental results show that the performance of QML-AiNet is comparable to QML-CLONALG, a QML system using the clonal selection algorithm (CLONALG). More importantly, QML-AiNet with the modified mutation operator can significantly improve the scalability of QML and is much more efficient than QML-CLONALG.
AB - In this paper, we explore the application of Opt-AiNet, an immune network approach for search and optimisation problems, to learning qualitative models in the form of qualitative differential equations. The Opt-AiNet algorithm is adapted to qualitative model learning problems, resulting in the proposed system QML-AiNet. The potential of QML-AiNet to address the scalability and multimodal search space issues of qualitative model learning has been investigated. More importantly, to further improve the efficiency of QML-AiNet, we also modify the mutation operator according to the features of discrete qualitative model space. Experimental results show that the performance of QML-AiNet is comparable to QML-CLONALG, a QML system using the clonal selection algorithm (CLONALG). More importantly, QML-AiNet with the modified mutation operator can significantly improve the scalability of QML and is much more efficient than QML-CLONALG.
KW - qualitative model learning
KW - artificial immune systems
KW - immune network approach
KW - compartmental models
KW - qualitative reasoning
KW - qualitative differential equation
U2 - 10.1016/j.asoc.2014.11.008
DO - 10.1016/j.asoc.2014.11.008
M3 - Article
VL - 27
SP - 148
EP - 157
JO - Applied Soft Computing
JF - Applied Soft Computing
SN - 1568-4946
ER -