%0 Book Section
%@holdercode {isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S}
%3 oliveira_population.pdf
%X This 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.
%E Gottlieb, J.,
%E Raidl, G.,
%T Population training heuristics
%@electronicmailaddress acmo@deinf.ufma.br
%@electronicmailaddress lorena@lac.inpe.br
%@secondarytype PRE LI
%K Hybrid evolutionary algorithms, population training, MOSP, GMLP.
%@nexthigherunit 8JMKD3MGPCW/3ESGTTP
%B Lecture Notes in Computer Science
%@usergroup administrator
%@usergroup simone
%@group
%@group LAC-INPE-MCT-BR
%@secondarykey INPE--/
%2 sid.inpe.br/iris@1916/2005/12.14.15.16.06
%@affiliation Universidade Federal do Maranhão (UFMA)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@versiontype publisher
%P 166-176
%4 sid.inpe.br/iris@1916/2005/12.14.15.16
%D 2005
%@documentstage not transferred
%V 3448
%A Oliveira, Alexandre C. M.,
%A Lorena, Luiz Antonio Nogueira,
%@dissemination NTRSNASA; BNDEPOSITOLEGAL.
%@area COMP