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

Wei Pang, George M. Coghill

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

12 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 publicationGECCO '07: Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
PublisherAssociation for Computing Machinery
Pages2887–2894
Number of pages8
ISBN (Print)978-1-59593-698-1
DOIs
Publication statusPublished - 2007
Event9th Annual Genetic and Evolutionary Computation Conference - London, United Kingdom
Duration: 7 Jul 200711 Jul 2007

Conference

Conference9th Annual Genetic and Evolutionary Computation Conference
Abbreviated titleGECCO 2007
CountryUnited Kingdom
CityLondon
Period7/07/0711/07/07

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  • Cite this

    Pang, W., & Coghill, G. M. (2007). Modified clonal selection algorithm for learning qualitative compartmental models of metabolic systems. In GECCO '07: Proceedings of the 9th annual conference companion on Genetic and evolutionary computation (pp. 2887–2894). Association for Computing Machinery. https://doi.org/10.1145/1274000.1274049