Modelling Finite Populations
Modelling Finite Populations
It is common to model the dynamics of an evolutionary algorithm by considering a typical or representative population. We consider modelling a genetic algorithm using two different descriptions of this typical population. The first using cumulants describing the distribution of fitnesses, the second using the distribution itself. Empirically the cumulant description is found to give a much more accurate description of the evolution despite the fact that it gives a cruder description of the typical distribution. The reason for the success of the cumulant approach is because cumulants are self-averaging quantities. The meaning of this statement in this context is explored. The full distribution description is exact in the limit of an infinite population. It is shown that even in simple problems this limit is only approached for very large populations. Furthermore, the infinite population limit can give misleading predictions for the observable long time behaviour of an evolutionary algorithm.
99-114
Prügel-Bennett, A.
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de Jong, K.
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Poli, R.
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Rowe, J. E.
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2003
Prügel-Bennett, A.
83d285f0-05b0-4db8-a367-9a01d7afdbda
de Jong, K.
c4d5e001-e7e5-467a-8267-e5447fb01285
Poli, R.
23fc1065-a5ec-476d-b0d2-cae4b36d1ec5
Rowe, J. E.
8a72d696-0903-4e7c-81c0-71291a04ddec
Prügel-Bennett, A.
(2003)
Modelling Finite Populations.
de Jong, K., Poli, R. and Rowe, J. E.
(eds.)
Foundations of Genetic Algorithms 7, Spain.
.
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Abstract
It is common to model the dynamics of an evolutionary algorithm by considering a typical or representative population. We consider modelling a genetic algorithm using two different descriptions of this typical population. The first using cumulants describing the distribution of fitnesses, the second using the distribution itself. Empirically the cumulant description is found to give a much more accurate description of the evolution despite the fact that it gives a cruder description of the typical distribution. The reason for the success of the cumulant approach is because cumulants are self-averaging quantities. The meaning of this statement in this context is explored. The full distribution description is exact in the limit of an infinite population. It is shown that even in simple problems this limit is only approached for very large populations. Furthermore, the infinite population limit can give misleading predictions for the observable long time behaviour of an evolutionary algorithm.
More information
Published date: 2003
Additional Information:
Event Dates: September 2002 Address: San Francisco
Venue - Dates:
Foundations of Genetic Algorithms 7, Spain, 2002-09-01
Organisations:
Electronics & Computer Science
Identifiers
Local EPrints ID: 259028
URI: http://eprints.soton.ac.uk/id/eprint/259028
PURE UUID: 1dee32d4-af0a-46f4-a667-624b1366f73b
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Date deposited: 12 Mar 2004
Last modified: 14 Mar 2024 06:17
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Contributors
Author:
A. Prügel-Bennett
Editor:
K. de Jong
Editor:
R. Poli
Editor:
J. E. Rowe
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