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

Wei Pang; George M. Coghill

doi:10.1145/1274000.1274049

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

Wei Pang, George M. Coghill

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	GECCO '07: Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Publisher	Association for Computing Machinery
Pages	2887–2894
Number of pages	8
ISBN (Print)	978-1-59593-698-1
DOIs	https://doi.org/10.1145/1274000.1274049
Publication status	Published - 2007
Event	9th Annual Genetic and Evolutionary Computation Conference 2007 - London, United Kingdom Duration: 7 Jul 2007 → 11 Jul 2007

Conference

Conference	9th Annual Genetic and Evolutionary Computation Conference 2007
Abbreviated title	GECCO 2007
Country/Territory	United Kingdom
City	London
Period	7/07/07 → 11/07/07

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10.1145/1274000.1274049

Cite this

@inproceedings{8b1000b584ac401f8e3284d43fb0fec7,

title = "Modified clonal selection algorithm for learning qualitative compartmental models of metabolic systems",

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.",

author = "Wei Pang and Coghill, {George M.}",

year = "2007",

doi = "10.1145/1274000.1274049",

language = "English",

isbn = "978-1-59593-698-1",

pages = "2887–2894",

booktitle = "GECCO '07: Proceedings of the 9th annual conference companion on Genetic and evolutionary computation",

publisher = "Association for Computing Machinery",

address = "United States",

note = "9th Annual Genetic and Evolutionary Computation Conference 2007, GECCO 2007 ; Conference date: 07-07-2007 Through 11-07-2007",

}

Pang, W & Coghill, GM 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. Association for Computing Machinery, pp. 2887–2894, 9th Annual Genetic and Evolutionary Computation Conference 2007, London, United Kingdom, 7/07/07. https://doi.org/10.1145/1274000.1274049

Modified clonal selection algorithm for learning qualitative compartmental models of metabolic systems. / Pang, Wei; Coghill, George M.
GECCO '07: Proceedings of the 9th annual conference companion on Genetic and evolutionary computation. Association for Computing Machinery, 2007. p. 2887–2894.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

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AU - Pang, Wei

AU - Coghill, George M.

PY - 2007

Y1 - 2007

N2 - 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.

AB - 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.

U2 - 10.1145/1274000.1274049

DO - 10.1145/1274000.1274049

M3 - Conference contribution

SN - 978-1-59593-698-1

SP - 2887

EP - 2894

BT - GECCO '07: Proceedings of the 9th annual conference companion on Genetic and evolutionary computation

PB - Association for Computing Machinery

T2 - 9th Annual Genetic and Evolutionary Computation Conference 2007

Y2 - 7 July 2007 through 11 July 2007

ER -

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

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