A new hybrid evolutionary algorithm with quasi-simplex technique

Zhang, GL; Lu, HY; Zhang, GQ

A new hybrid evolutionary algorithm with quasi-simplex technique

Zhang, GL Lu, HY

Zhang, GQ

Permalink

Publication Type:: Conference Proceeding
Citation:: 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010, 2010, 4 pp. 1811 - 1816
Issue Date:: 2010-11-15

Closed Access

	Filename	Description	Size
	2010003107OK.pdf		214 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Zhang, GL	en_US
dc.contributor.author	Lu, HY https://orcid.org/0000-0001-5655-0237	en_US
dc.contributor.author	Zhang, GQ https://orcid.org/0000-0003-3960-0583	en_US
dc.date.issued	2010-11-15	en_US
dc.identifier.citation	2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010, 2010, 4 pp. 1811 - 1816	en_US
dc.identifier.isbn	9781424465262	en_US
dc.identifier.uri	http://hdl.handle.net/10453/16160
dc.description.abstract	This paper proposes a new parallel search algorithm using an evolutionary algorithm and quasi-simplex techniques (EAQST) for non-linear constrained function optimization. EAQST produces the offspring in parallel by using the Gaussian mutation, the Cauchy mutation and the quasi-simplex technique. Experimental studies on typical benchmark functions have shown that EAQST has very better performance than the compared algorithm. © 2010 IEEE.	en_US
dc.relation.ispartof	2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010	en_US
dc.relation.isbasedon	10.1109/ICMLC.2010.5580962	en_US
dc.title	A new hybrid evolutionary algorithm with quasi-simplex technique	en_US
dc.type	Conference Proceeding
utslib.citation.volume	4	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Qingdao, China	en_US
dc.location.activity	ISI:000177208000009
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Systems, Management and Leadership
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	4	en_US

Abstract:

This paper proposes a new parallel search algorithm using an evolutionary algorithm and quasi-simplex techniques (EAQST) for non-linear constrained function optimization. EAQST produces the offspring in parallel by using the Gaussian mutation, the Cauchy mutation and the quasi-simplex technique. Experimental studies on typical benchmark functions have shown that EAQST has very better performance than the compared algorithm. © 2010 IEEE.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/16160