A New Differential Evolution with self-terminating ability using fuzzy control and k-nearest neighbors

Lai, JC; Leung, FH; Ling, SH

A New Differential Evolution with self-terminating ability using fuzzy control and k-nearest neighbors

Lai, JC Leung, FH Ling, SH

Permalink

Publication Type:: Conference Proceeding
Citation:: 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010, 2010
Issue Date:: 2010-12-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download full textAdobe PDF (314.28 kB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Lai, JC	en_US
dc.contributor.author	Leung, FH	en_US
dc.contributor.author	Ling, SH https://orcid.org/0000-0003-0849-5098	en_US
dc.date.issued	2010-12-01	en_US
dc.identifier.citation	2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010, 2010	en_US
dc.identifier.isbn	9781424469109	en_US
dc.identifier.uri	http://hdl.handle.net/10453/16248
dc.description.abstract	A new Differential Evolution (DE) that incorporates fuzzy control and k-nearest neighbors algorithm to determine the terminating condition is proposed. A technique called Iteration Windows is introduced to govern the number of iteration in each searching stage. The size of the iteration windows is controlled by a fuzzy controller, which uses the information provided by the k-nearest neighbors system to analyze the population during the searching process. The controller keeps controlling the iteration windows until the end of the searching process. The wavelet based mutation process is embedded in the DE searching process to enhance the searching performance of DE. The F weight of DE is also controlled by the fuzzy controller to further speed up the searching process. A suite of benchmark test functions is employed to evaluate the performance of the proposed method. It is shown empirically that the proposed method can terminate the searching process with a reasonable number of iteration. © 2010 IEEE.	en_US
dc.relation.ispartof	2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010	en_US
dc.relation.isbasedon	10.1109/CEC.2010.5586058	en_US
dc.title	A New Differential Evolution with self-terminating ability using fuzzy control and k-nearest neighbors	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Barcelona, Spain	en_US
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 Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

A new Differential Evolution (DE) that incorporates fuzzy control and k-nearest neighbors algorithm to determine the terminating condition is proposed. A technique called Iteration Windows is introduced to govern the number of iteration in each searching stage. The size of the iteration windows is controlled by a fuzzy controller, which uses the information provided by the k-nearest neighbors system to analyze the population during the searching process. The controller keeps controlling the iteration windows until the end of the searching process. The wavelet based mutation process is embedded in the DE searching process to enhance the searching performance of DE. The F weight of DE is also controlled by the fuzzy controller to further speed up the searching process. A suite of benchmark test functions is employed to evaluate the performance of the proposed method. It is shown empirically that the proposed method can terminate the searching process with a reasonable number of iteration. © 2010 IEEE.

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

http://hdl.handle.net/10453/16248