Hybrid genetic algorithm based on distance density and quasi-simplex technique

Zhang, G; Xie, H

Hybrid genetic algorithm based on distance density and quasi-simplex technique

Zhang, G

Xie, H

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Publication Type:: Conference Proceeding
Citation:: World Scientific Proceedings Series on Computer Engineering and Information Science 1; Computational Intelligence in Decision and Control - Proceedings of the 8th International FLINS Conference, 2008, pp. 737 - 742
Issue Date:: 2008-12-01

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Field	Value	Language
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583	en_US
dc.contributor.author	Xie, H	en_US
dc.date.issued	2008-12-01	en_US
dc.identifier.citation	World Scientific Proceedings Series on Computer Engineering and Information Science 1; Computational Intelligence in Decision and Control - Proceedings of the 8th International FLINS Conference, 2008, pp. 737 - 742	en_US
dc.identifier.isbn	981279946X	en_US
dc.identifier.isbn	9789812799463	en_US
dc.identifier.uri	http://hdl.handle.net/10453/10713
dc.description.abstract	This paper introduces first the concept of distance density, and then proposes a new hybrid genetic algorithm based on distance density and quasi-simplex technique (HGABDDQT). HGABDDQT produces the offspring using the genetic operations and the quasi-simplex technique in parallel. In genetic operations, the crossover probability is determined adaptively by distance density, the mutation probability is determined adaptively by distance density and fitness. No binary encoding/decoding in mutation and crossover operations. HGABDDQT algorithm has been implemented and tested on typical benchmark functions. The experimental study has shown that HGABDDQT is more effective than the competitive algorithm in finding the near global optimal solutions.	en_US
dc.relation.ispartof	World Scientific Proceedings Series on Computer Engineering and Information Science 1; Computational Intelligence in Decision and Control - Proceedings of the 8th International FLINS Conference	en_US
dc.title	Hybrid genetic algorithm based on distance density and quasi-simplex technique	en_US
dc.type	Conference Proceeding
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	en_US
utslib.for	080105 Expert Systems	en_US
dc.location.activity	Madrid, 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 Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

This paper introduces first the concept of distance density, and then proposes a new hybrid genetic algorithm based on distance density and quasi-simplex technique (HGABDDQT). HGABDDQT produces the offspring using the genetic operations and the quasi-simplex technique in parallel. In genetic operations, the crossover probability is determined adaptively by distance density, the mutation probability is determined adaptively by distance density and fitness. No binary encoding/decoding in mutation and crossover operations. HGABDDQT algorithm has been implemented and tested on typical benchmark functions. The experimental study has shown that HGABDDQT is more effective than the competitive algorithm in finding the near global optimal solutions.

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http://hdl.handle.net/10453/10713