Genetic Drift in Genetic Algorithm Selection Schemes
Genetic Drift in Genetic Algorithm Selection Schemes
A method for calculating genetic drift in terms of changing population fitness variance is presented. The method allows for an easy comparison of different selection schemes and exact analytical results are derived for traditional generational selection, steady-state selection with varying generation gap, a simple model of Eshelman's CHC algorithm, and evolution strategies. The effects of changing genetic drift on the convergence of a GA are demonstrated empirically.
298-303
Rogers, A.
f9130bc6-da32-474e-9fab-6c6cb8077fdc
Prügel-Bennett, A.
b107a151-1751-4d8b-b8db-2c395ac4e14e
October 1999
Rogers, A.
f9130bc6-da32-474e-9fab-6c6cb8077fdc
Prügel-Bennett, A.
b107a151-1751-4d8b-b8db-2c395ac4e14e
Rogers, A. and Prügel-Bennett, A.
(1999)
Genetic Drift in Genetic Algorithm Selection Schemes.
IEEE Transactions on Evolutionary Computation, 3 (4), .
Abstract
A method for calculating genetic drift in terms of changing population fitness variance is presented. The method allows for an easy comparison of different selection schemes and exact analytical results are derived for traditional generational selection, steady-state selection with varying generation gap, a simple model of Eshelman's CHC algorithm, and evolution strategies. The effects of changing genetic drift on the convergence of a GA are demonstrated empirically.
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Published date: October 1999
Organisations:
Agents, Interactions & Complexity, Southampton Wireless Group
Identifiers
Local EPrints ID: 250688
URI: http://eprints.soton.ac.uk/id/eprint/250688
PURE UUID: 042dc186-d68a-4b61-b011-88da739d8d7e
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Date deposited: 31 Mar 2000
Last modified: 14 Mar 2024 04:54
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Contributors
Author:
A. Rogers
Author:
A. Prügel-Bennett
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