Abstract
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.
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 |
Bibliographical note
AcknowledgementsWP 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.
Keywords
- qualitative model learning
- artificial immune systems
- immune network approach
- compartmental models
- qualitative reasoning
- qualitative differential equation