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1. Identity statement
Reference TypeJournal Article
Sitemtc-m16.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier6qtX3pFwXQZsFDuKxG/GEAEg
Repositorysid.inpe.br/marciana/2005/07.05.12.38   (restricted access)
Last Update2010:08.31.14.12.44 (UTC) marciana
Metadata Repositorysid.inpe.br/marciana/2005/07.05.12.38.12
Metadata Last Update2018:06.05.01.16.46 (UTC) administrator
Secondary KeyINPE--PRE/
ISSN0302-9743
Citation KeyOliveiraLore:2005:PoTrHe
TitlePopulation training heuristics
Year2005
Access Date2024, Apr. 27
Secondary TypePRE PI
Number of Files1
Size227 KiB
2. Context
Author1 Oliveira, A. C. M.
2 Lorena, Luiz Aantonio Nogueira
Group1 LAC-INPE-MCT-BR
2 LAC-INPE-MCT-BR
Affiliation1 Universidade Federal Maranhao, Dept Informat, S Luis, MA Brazil
2 Instituto Nacional de Pesquisas Espaciais, Laboratório Associado de Computação e Matemática Aplicada(INPE, LAC) (INPE)
JournalEvolutionary Computation in Combinatorial Optimization Lecture Notes in Computer Science
Volume3448
Pages166-176
History (UTC)2005-07-05 12:38:14 :: sergio -> administrator ::
2007-04-03 15:28:04 :: administrator -> sergio ::
2008-01-07 12:54:03 :: sergio -> administrator ::
2010-05-11 20:17:27 :: administrator -> marciana ::
2010-08-31 14:12:45 :: marciana -> administrator ::
2012-10-22 22:33:44 :: administrator -> marciana :: 2005
2013-02-28 18:24:40 :: marciana -> administrator :: 2005
2018-06-05 01:16:46 :: administrator -> marciana :: 2005
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
Keywordshybrid evolutionary algorithms
population training
MOSP
GMLP
EVOLUTIONARY APPROACH
AbstractThis work describes a new way of employing problem-specific heuristics to improve evolutionary algorithms: the Population Training Heuristic (PTH). The PTH employs heuristics in fitness definition, guiding the population to settle down in search areas where the individuals can not be improved by such heuristics. Some new theoretical improvements not present in early algorithms are now introduced. An application for pattern sequencing problems is examined with new improved computational results. The method is also compared against other approaches, using benchmark instances taken from the literature.
AreaCOMP
Arrangementurlib.net > BDMCI > Fonds > Produção anterior à 2021 > LABAC > Population training heuristics
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Contentthere are no files
4. Conditions of access and use
Languageen
Target FileACMOliveira.pdf
User Groupadministrator
marciana
sergio
Visibilityshown
Archiving Policydenypublisher denyfinaldraft12
Read Permissiondeny from all and allow from 150.163
Update Permissionnot transferred
5. Allied materials
Next Higher Units8JMKD3MGPCW/3ESGTTP
DisseminationWEBSCI
Host Collectionsid.inpe.br/banon/2003/08.15.17.40
6. Notes
Notes5th European Conference on Evolutionary Computation in Combinatorial Optimization. Lausanne, MAR 30-APR 01, 2005
Empty Fieldsalternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel doi e-mailaddress electronicmailaddress format isbn label lineage mark mirrorrepository month nextedition number orcid parameterlist parentrepositories previousedition previouslowerunit progress project readergroup resumeid rightsholder schedulinginformation secondarydate secondarymark session shorttitle sponsor subject tertiarymark tertiarytype typeofwork url
7. Description control
e-Mail (login)marciana
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