In this paper, a modified Clonal Selection Algorithm (CSA)is proposed to learn qualitative compartmental models. Different from traditional AI search algorithm, this population based approach employs antibody repertoire to perform random search, which is suitable for the ragged and multi-modal landscape of qualitative model space. Experimental result shows that this algorithm can obtain the same kernel sets and learning reliability as previous work for learning the two compartment model, and it can also search out the target model when learning the more complex three-compartment model. Although this algorithm does not succeed in learning the four-compartment model, promising result is still obtained.
|Title of host publication||Proceedings of the 2007 GECCO conference companion on Genetic and evolutionary computation|
|Number of pages||8|
|Publication status||Published - 2007|