When A Genetic Algorithm Outperforms Hill-Climbing
When A Genetic Algorithm Outperforms Hill-Climbing
A toy optimisation problem is introduced which consists of a fitness gradient broken up by a series of hurdles. The performance of a hill-climber and a stochastic hill-climber are computed. These are compared with the empirically observed performance of a genetic algorithm (GA) with and without. The hill-climber with a sufficiently large neighbourhood outperforms the stochastic hill-climber, but is outperformed by a GA both with and without crossover. The GA with crossover substantially outperforms all the other heuristics considered here. The relevance of this result to real world problems is discussed.
135-153
Prügel-Bennett, A.
b107a151-1751-4d8b-b8db-2c395ac4e14e
June 2004
Prügel-Bennett, A.
b107a151-1751-4d8b-b8db-2c395ac4e14e
Prügel-Bennett, A.
(2004)
When A Genetic Algorithm Outperforms Hill-Climbing.
Theoretical Computer Science, 320 (1), .
Abstract
A toy optimisation problem is introduced which consists of a fitness gradient broken up by a series of hurdles. The performance of a hill-climber and a stochastic hill-climber are computed. These are compared with the empirically observed performance of a genetic algorithm (GA) with and without. The hill-climber with a sufficiently large neighbourhood outperforms the stochastic hill-climber, but is outperformed by a GA both with and without crossover. The GA with crossover substantially outperforms all the other heuristics considered here. The relevance of this result to real world problems is discussed.
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Published date: June 2004
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Accepted for Publication
Organisations:
Southampton Wireless Group
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Local EPrints ID: 259123
URI: http://eprints.soton.ac.uk/id/eprint/259123
ISSN: 0304-3975
PURE UUID: 04697e88-a205-4540-a7ca-eaab80de3dac
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Date deposited: 23 May 2004
Last modified: 14 Mar 2024 06:19
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Author:
A. Prügel-Bennett
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