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.
|Title of host publication||ICARIS 2010: Artificial Immune Systems|
|Editors||Emma Hart, Chris McEwan, Jon Timmis, Andy Hone|
|Place of Publication||Berlin, Heidelberg|
|Number of pages||14|
|Publication status||Published - 2010|
|Name|| Lecture Notes in Computer Science|