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 candidate 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.
Pang, W., & Coghill, G. M. (2010). Learning Qualitative Metabolic Models Using Evolutionary Methods. In 2010 Fifth International Conference on Frontier of Computer Science and Technology IEEE. https://doi.org/10.1109/FCST.2010.57