When A Genetic Algorithm Outperforms Hill-Climbing
Prügel-Bennett, A. (2004) When A Genetic Algorithm Outperforms Hill-Climbing. Theoretical Computer Science, 320, (1), 135-153.
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Description/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.
| Item Type: | Article |
|---|---|
| Additional Information: | Accepted for Publication |
| Divisions: | Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Comms, Signal Processing & Control |
| Item ID: | 259123 |
| Date Deposited: | 23 May 2004 |
| Last Modified: | 01 Mar 2012 10:59 |
| Contributors: | Prügel-Bennett, A. (Author) |
| Date: | June 2004 |
| Additional Information: | Accepted for Publication |
| Status: | Published |
| Further Information: | Google Scholar |
| ISI Citation Count: | 14 |
| URI: | http://eprints.soton.ac.uk/id/eprint/259123 |
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