QML-AiNet: an immune network approach to learning qualitative differential equation models

Research output: Contribution to journalArticle

5 Citations (Scopus)
7 Downloads (Pure)

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 languageEnglish
Pages (from-to)148-157
Number of pages10
JournalApplied Soft Computing
Volume27
Early online date20 Nov 2014
DOIs
Publication statusPublished - Feb 2015

Keywords

  • qualitative model learning
  • artificial immune systems
  • immune network approach
  • compartmental models
  • qualitative reasoning
  • qualitative differential equation

Fingerprint Dive into the research topics of 'QML-AiNet: an immune network approach to learning qualitative differential equation models'. Together they form a unique fingerprint.

  • Cite this