Modified clonal selection algorithm for learning qualitative compartmental models of metabolic systems

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

11 Citations (Scopus)

Abstract

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.
Original languageEnglish
Title of host publicationProceedings of the 2007 GECCO conference companion on Genetic and evolutionary computation
PublisherACM Press
Pages2887-2894
Number of pages8
ISBN (Electronic)9781595936981
DOIs
Publication statusPublished - 2007

    Fingerprint

Cite this

Pang, W., & Coghill, G. M. (2007). Modified clonal selection algorithm for learning qualitative compartmental models of metabolic systems. In Proceedings of the 2007 GECCO conference companion on Genetic and evolutionary computation (pp. 2887-2894). ACM Press. https://doi.org/10.1145/1274000.1274049