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gLINC: Identifying composability using group perturbation

Coffin, DJ; Clack, CD; (2006) gLINC: Identifying composability using group perturbation. In: Keijzer, M, (ed.) Proceedings of the 8th annual conference on Genetic and evolutionary computation. (pp. pp. 1133-1140). ACM: Association for Computing Machinery: Seattle, WA, USA. Green open access

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Abstract

We present two novel perturbation-based linkage learning algorithms that extend LINC [5]; a version of LINC optimised for decomposition tasks (oLINC) and a hierarchical version of oLINC (gLINC). We show how gLINC decomposes a fitness landscape significantly faster than both LINC and oLINC.We present details of LINC, oLINC and gLINC, an empirical comparison of their speed, accuracy and sensitivity to population size on a concatenated trap function, and a discussion of their complexity and correctness.

Type: Proceedings paper
Title: gLINC: Identifying composability using group perturbation
Event: 8th Annual Genetic and Evolutionary Computation Conference
Location: Seattle, WA
Dates: 08 July 2006 - 12 July 2006
ISBN-13: 978-1-59593-186-3
Open access status: An open access version is available from UCL Discovery
DOI: 10.1145/1143997.1144178
Publisher version: http://dx.doi.org/10.1145/1143997.1144178
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Science & Technology, Technology, Computer Science, Artificial Intelligence, Computer Science, Software Engineering, Computer Science, genetic algorithms, linkage learning, epistasis, composition, perturbation, hierarchical
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10087112
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