Andrei Petrovski
Statistical optimisation and tuning of GA factors.
Petrovski, Andrei; Brownlee, Alexander; McCall, John
Abstract
This paper presents a practical methodology of improving the efficiency of Genetic Algorithms through tuning the factors significantly affecting GA performance. This methodology is based on the methods of statistical inference and has been successfully applied to both binary- and integer-encoded Genetic Algorithms that search for good chemotherapeutic schedules.
Citation
PETROVSKI, A., BROWNLEE, A. and MCCALL, J. 2005. Statistical optimisation and tuning of GA factors. In Proceedings of the 2005 IEEE congress on evolutionary computation (CEC 2005), 2-5 September 2005, Edinburgh, UK. New York: IEEE [online], volume 1, article number 1554759, pages 758-764. Available from: https://doi.org/10.1109/CEC.2005.1554759
Presentation Conference Type | Conference Paper (published) |
---|---|
Conference Name | 2005 IEEE congress on evolutionary computation (CEC 2005) |
Start Date | Sep 2, 2005 |
End Date | Sep 5, 2005 |
Acceptance Date | Sep 30, 2005 |
Online Publication Date | Sep 30, 2005 |
Publication Date | Dec 31, 2005 |
Deposit Date | Oct 20, 2009 |
Publicly Available Date | Oct 20, 2009 |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Peer Reviewed | Peer Reviewed |
Article Number | 1554759 |
Pages | 758-764 |
Series Title | IEEE transactions on evolutionary computation |
ISBN | 0780393635 |
DOI | https://doi.org/10.1109/CEC.2005.1554759 |
Keywords | Genetic algorithms; Algorithm design and analysis; Performance analysis; Analysis of variance; Space exploration; Processor scheduling; Computer science; Application software; Encoding; Genetic engineering |
Public URL | http://hdl.handle.net/10059/433 |
Contract Date | Oct 20, 2009 |
Files
PETROVSKI 2005 Statistical optimisation and tuning
(243 Kb)
PDF
Publisher Licence URL
https://creativecommons.org/licenses/by-nc-nd/4.0/
You might also like
Two-layer ensemble of deep learning models for medical image segmentation.
(2024)
Journal Article
DEFEG: deep ensemble with weighted feature generation.
(2023)
Journal Article
A comparative study of anomaly detection methods for gross error detection problems.
(2023)
Journal Article
Downloadable Citations
About OpenAIR@RGU
Administrator e-mail: publications@rgu.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2025
Advanced Search