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||proc. of 8th International Conference on Artificial Immune Systems (ICARIS 2010)|
|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|
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). Berlin Heidelberg: Springer-Verlag. https://doi.org/10.1007/978-3-642-14547-6_18