title: QML-AiNet: An immune network approach to learning qualitative differential equation models.
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.
authors: School of Natural and Computing Sciences, University of Aberdeen, Aberdeen AB24 3UE, UK.
authors: Wei
authors: W
authors: Pang
authors: School of Natural and Computing Sciences, University of Aberdeen, Aberdeen AB24 3UE, UK.
authors: George M
authors: GM
authors: Coghill
keywords: Artificial immune systems; Compartmental models; Immune network approach; Qualitative differential equation; Qualitative model learning; Qualitative reasoning
references: J Appl Physiol (1985). 2000 Aug;89(2):613-20
references: 10926645
references: Knowl Eng Rev. 2010 Mar;25(1):69-107
references: 23704803