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

Wei Pang, George MacLeod Coghill

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 publicationICARIS 2010: Artificial Immune Systems
EditorsEmma Hart, Chris McEwan, Jon Timmis, Andy Hone
PublisherSpringer Heidelberg
Pages223-236
Number of pages14
ISBN (Electronic)978-3-642-14547-6
ISBN (Print)978-3-642-14546-9
DOIs
Publication statusPublished - 2010

Publication series

Name Lecture Notes in Computer Science
Volume6209
ISSN (Print)0302-9743

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