QML-AiNet: An Immune-inspired Network Approach to Qualitative Model Learning

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

In this paper we continue the research on applying immune- inspired algorithms as search strategies to Qualitative Model Learning (QML). A new search strategy based on opt-AiNet is proposed, and this results in the development of a novel QML system called QML-AiNet. The performance of QML-AiNet is compared with previous work us- ing the CLONALG approach. Experimental results shows that although not as efficient as CLONALG, the opt-AiNet based approach still shows promising results for learning qualitative models. In addition, possible fu- ture work to further improve the efficiency of QML-AiNet is also pointed out.
Original languageEnglish
Title of host publicationproc. of 8th International Conference on Artificial Immune Systems (ICARIS 2010)
EditorsEmma Hart, Chris McEwan, Jon Timmis, Andy Hone
Place of PublicationBerlin Heidelberg
PublisherSpringer-Verlag
Pages223-236
Number of pages14
ISBN (Print)978-3-642-14546-9
DOIs
Publication statusPublished - 2010

Publication series

NameLecture Notes in Computer Science
PublisherSpringer-Verlag
Volume6209

Fingerprint Dive into the research topics of 'QML-AiNet: An Immune-inspired Network Approach to Qualitative Model Learning'. Together they form a unique fingerprint.

  • Cite this

    Pang, W., & Coghill, G. M. (2010). QML-AiNet: An Immune-inspired Network Approach to Qualitative Model Learning. In E. Hart, C. McEwan, J. Timmis, & A. Hone (Eds.), proc. of 8th International Conference on Artificial Immune Systems (ICARIS 2010) (pp. 223-236). (Lecture Notes in Computer Science; Vol. 6209). Springer-Verlag. https://doi.org/10.1007/978-3-642-14547-6_18