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.
|Title of host publication||2nd European Symposium on Nature-inspired Smart Information Systems|
|Place of Publication||Puerto de la Cruz, Tenerife, Spain|
|Number of pages||7|
|Publication status||Published - 2006|
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). http://www.nisis.risk-technologies.com/(S(qg2bvp45uzhies45040gi1nz))/Events/Symp2006/Papers/AB14_Coghill_Pang.pdf