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

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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

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Differential equations
Scalability

Keywords

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

Cite this

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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.",
keywords = "qualitative model learning, artificial immune systems, immune network approach, compartmental models, qualitative reasoning, qualitative differential equation",
author = "Wei Pang and Coghill, {George M}",
note = "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.",
year = "2015",
month = "2",
doi = "10.1016/j.asoc.2014.11.008",
language = "English",
volume = "27",
pages = "148--157",
journal = "Applied Soft Computing",
issn = "1568-4946",
publisher = "Elsevier BV",

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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

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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

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JO - Applied Soft Computing

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SN - 1568-4946

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