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An analysis of genetic algorithms using statistical mechanics.

An analysis of genetic algorithms using statistical mechanics.
An analysis of genetic algorithms using statistical mechanics.
A formalism is presented for modelling the evolutionary dynamics of a population of gene sequences. The formalism was originally developed for describing genetic algorithms. In this paper the formalism is elaborated by considering the evolution of an ensemble of populations. This allows the evolution to be modelled more accurately. To illustrate the formalism the problem of a population of gene sequences evolving in a multiplicative fitness landscape is considered. A comparison with simulations is made and shows very good agreement. More complicated problems have already been investigated including sexual recombination and evolution in a multi-valleyed fitness landscape. These results will be briefly reviewed.
0167-2789
75-114
Prügel-Bennett, A.
b107a151-1751-4d8b-b8db-2c395ac4e14e
Shapiro, J. L.
6bdedc11-bbe8-4646-9de4-9e7b4d9bab0e
Prügel-Bennett, A.
b107a151-1751-4d8b-b8db-2c395ac4e14e
Shapiro, J. L.
6bdedc11-bbe8-4646-9de4-9e7b4d9bab0e

Prügel-Bennett, A. and Shapiro, J. L. (1997) An analysis of genetic algorithms using statistical mechanics. Physica D: Nonlinear Phenomena, 104, 75-114.

Record type: Article

Abstract

A formalism is presented for modelling the evolutionary dynamics of a population of gene sequences. The formalism was originally developed for describing genetic algorithms. In this paper the formalism is elaborated by considering the evolution of an ensemble of populations. This allows the evolution to be modelled more accurately. To illustrate the formalism the problem of a population of gene sequences evolving in a multiplicative fitness landscape is considered. A comparison with simulations is made and shows very good agreement. More complicated problems have already been investigated including sexual recombination and evolution in a multi-valleyed fitness landscape. These results will be briefly reviewed.

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Published date: 1997
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 250689
URI: https://eprints.soton.ac.uk/id/eprint/250689
ISSN: 0167-2789
PURE UUID: 247b4784-ba98-4e8b-8b53-14aa72ad02b5

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Date deposited: 17 Aug 1999
Last modified: 19 Jul 2019 22:50

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Contributors

Author: A. Prügel-Bennett
Author: J. L. Shapiro

University divisions

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