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

被引：0

作者：

Pang, Wei ^{[1
]}

Coghill, George M. ^{[1
]}

机构：

[1] Univ Aberdeen, Dept Comp Sci, Aberdeen AB24 3UE, Scotland

来源：

ARTIFICIAL IMMUNE SYSTEMS | 2010年 / 6209卷

关键词：

SYSTEM-IDENTIFICATION; ALGORITHM;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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 using 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 future work to further improve the efficiency of QML-AiNet is also pointed out.

引用

页码：223 / 236

页数：14

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