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Article: A robust iterative hypothesis testing design of the repeated genetic algorithm

TitleA robust iterative hypothesis testing design of the repeated genetic algorithm
Authors
Issue Date2005
Citation
Image and Vision Computing, 2005, v. 23 n. 11, p. 972-980 How to Cite?
AbstractThe genetic algorithm is a simple and interesting optimization method for a wide variety of computer vision problems. However, its performance is often brittle and degrades drastically with increasing input problem complexity. While this problem is difficult to overcome due to the stochastic nature of the algorithm, this paper shows that a robust statistical design using sequential sampling, repeated independent trials and hypothesis testing can be used to greatly alleviate the degradation. The working principle is as follows: The probability of success P of a stochastic algorithm A (in this case A is the genetic algorithm) can be estimated by running N copies of A simultaneously or running A repeatedly N times. Such a scheme is generally referred to as the parallel or repeated (genetic) algorithm. By hypothesis testing, P can be tested with a required figure of merit (i.e. the level of significance). This is used in turn to adjust N in an iterative scheme to maintain a constant P repeated, achieving a robust feedback loop. Experimental results on both synthetic and real images are reported on the application of this novel algorithm to an affine object detection problem and a free form 3D object registration problem. © 2005 Elsevier B.V. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/196664
ISSN
2015 Impact Factor: 1.766
2015 SCImago Journal Rankings: 1.700
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorYuen, SY-
dc.contributor.authorLam, HS-
dc.contributor.authorFong, CK-
dc.contributor.authorChen, SF-
dc.contributor.authorChow, CK-
dc.date.accessioned2014-04-24T02:10:32Z-
dc.date.available2014-04-24T02:10:32Z-
dc.date.issued2005-
dc.identifier.citationImage and Vision Computing, 2005, v. 23 n. 11, p. 972-980-
dc.identifier.issn0262-8856-
dc.identifier.urihttp://hdl.handle.net/10722/196664-
dc.description.abstractThe genetic algorithm is a simple and interesting optimization method for a wide variety of computer vision problems. However, its performance is often brittle and degrades drastically with increasing input problem complexity. While this problem is difficult to overcome due to the stochastic nature of the algorithm, this paper shows that a robust statistical design using sequential sampling, repeated independent trials and hypothesis testing can be used to greatly alleviate the degradation. The working principle is as follows: The probability of success P of a stochastic algorithm A (in this case A is the genetic algorithm) can be estimated by running N copies of A simultaneously or running A repeatedly N times. Such a scheme is generally referred to as the parallel or repeated (genetic) algorithm. By hypothesis testing, P can be tested with a required figure of merit (i.e. the level of significance). This is used in turn to adjust N in an iterative scheme to maintain a constant P repeated, achieving a robust feedback loop. Experimental results on both synthetic and real images are reported on the application of this novel algorithm to an affine object detection problem and a free form 3D object registration problem. © 2005 Elsevier B.V. All rights reserved.-
dc.languageeng-
dc.relation.ispartofImage and Vision Computing-
dc.titleA robust iterative hypothesis testing design of the repeated genetic algorithm-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.imavis.2005.07.001-
dc.identifier.scopuseid_2-s2.0-25144484476-
dc.identifier.volume23-
dc.identifier.issue11-
dc.identifier.spage972-
dc.identifier.epage980-
dc.identifier.isiWOS:000232520600004-

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