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The Dynamics of a Genetic Algorithm on a Model Hard Optimization Problem

The Dynamics of a Genetic Algorithm on a Model Hard Optimization Problem
The Dynamics of a Genetic Algorithm on a Model Hard Optimization Problem
A model of a hard optimization problem suggested in the literature is considered. The dynamics of a genetic algorithm (GA) using ranking selection, mutation and uniform crossover are completely modeled on this problem. These results are general and are valid for any symmetrical concave function of unitation. Full finite population effects are taken into account allowing a novel analytical comparison of roulette wheel and stochastic universal sampling. Closed form expressions are derived for the equilibrium population distribution of this model. The first passage time to move from a local to a global minimum in a two potential well landscape is calculated. A comparison is made with a stochastic hill climber and a GA without crossover. The GA with crossover is shown to perform orders of magnitude faster giving some insights into the nature of GA search and the crossover operator on these types of problem.
437-64
Rogers, A.
f9130bc6-da32-474e-9fab-6c6cb8077fdc
Prügel-Bennett, A.
b107a151-1751-4d8b-b8db-2c395ac4e14e
Rogers, A.
f9130bc6-da32-474e-9fab-6c6cb8077fdc
Prügel-Bennett, A.
b107a151-1751-4d8b-b8db-2c395ac4e14e

Rogers, A. and Prügel-Bennett, A. (2000) The Dynamics of a Genetic Algorithm on a Model Hard Optimization Problem. Complex Systems, 11 (6), 437-64.

Record type: Article

Abstract

A model of a hard optimization problem suggested in the literature is considered. The dynamics of a genetic algorithm (GA) using ranking selection, mutation and uniform crossover are completely modeled on this problem. These results are general and are valid for any symmetrical concave function of unitation. Full finite population effects are taken into account allowing a novel analytical comparison of roulette wheel and stochastic universal sampling. Closed form expressions are derived for the equilibrium population distribution of this model. The first passage time to move from a local to a global minimum in a two potential well landscape is calculated. A comparison is made with a stochastic hill climber and a GA without crossover. The GA with crossover is shown to perform orders of magnitude faster giving some insights into the nature of GA search and the crossover operator on these types of problem.

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Published date: May 2000
Organisations: Agents, Interactions & Complexity, Southampton Wireless Group

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Local EPrints ID: 250881
URI: https://eprints.soton.ac.uk/id/eprint/250881
PURE UUID: 722f7dc0-ace4-4ab1-bcf9-912b2dff7ab6

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Date deposited: 29 May 2001
Last modified: 18 Jul 2017 10:13

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Contributors

Author: A. Rogers
Author: A. Prügel-Bennett

University divisions

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