EQML- An Evolutionary Qualitative Model Learning Framework

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Abstract

In this paper, an Evolutionary Qualitative Model Learning Framework (EQML) is proposed and tested by learning the qualitative metabolic models under the condition of incomplete knowledge. JMorven, a fuzzy qualitative reasoning engine, is slightly modified and integrated into the framework as a sub module to represent and verify the learnt models. Three metabolic compartment models are tested by two evolutionary algorithms (Genetic Algorithm and Clonal Selection Algorithm) in EQML. Finally the efficiency of these two algorithms is evaluated.
Original languageEnglish
Title of host publication2nd European Symposium on Nature-inspired Smart Information Systems
Place of PublicationPuerto de la Cruz, Tenerife, Spain
Pages1-7
Number of pages7
Publication statusPublished - 2006

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Evolutionary algorithms
Genetic algorithms
Engines

Cite this

Pang, W., & Coghill, G. M. (2006). EQML- An Evolutionary Qualitative Model Learning Framework. In 2nd European Symposium on Nature-inspired Smart Information Systems (pp. 1-7). Puerto de la Cruz, Tenerife, Spain.

EQML- An Evolutionary Qualitative Model Learning Framework. / Pang, Wei; Coghill, George MacLeod.

2nd European Symposium on Nature-inspired Smart Information Systems. Puerto de la Cruz, Tenerife, Spain, 2006. p. 1-7.

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

Pang, W & Coghill, GM 2006, EQML- An Evolutionary Qualitative Model Learning Framework. in 2nd European Symposium on Nature-inspired Smart Information Systems. Puerto de la Cruz, Tenerife, Spain, pp. 1-7.
Pang W, Coghill GM. EQML- An Evolutionary Qualitative Model Learning Framework. In 2nd European Symposium on Nature-inspired Smart Information Systems. Puerto de la Cruz, Tenerife, Spain. 2006. p. 1-7
Pang, Wei ; Coghill, George MacLeod. / EQML- An Evolutionary Qualitative Model Learning Framework. 2nd European Symposium on Nature-inspired Smart Information Systems. Puerto de la Cruz, Tenerife, Spain, 2006. pp. 1-7
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