%0 Journal Article
%@holdercode {isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S}
%@nexthigherunit 8JMKD3MGPCW/3ESGTTP
%@archivingpolicy denypublisher denyfinaldraft12
%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.
%T Population training heuristics
%@secondarytype PRE PI
%K hybrid evolutionary algorithms, population training, MOSP, GMLP, EVOLUTIONARY APPROACH.
%@usergroup administrator
%@usergroup marciana
%@usergroup sergio
%@group LAC-INPE-MCT-BR
%@group LAC-INPE-MCT-BR
%3 ACMOliveira.pdf
%@secondarykey INPE--PRE/
%@issn 0302-9743
%2 sid.inpe.br/marciana/2005/07.05.12.38.12
%@affiliation Universidade Federal Maranhao, Dept Informat, S Luis, MA Brazil
%@affiliation Instituto Nacional de Pesquisas Espaciais, Laboratório Associado de Computação e Matemática Aplicada(INPE, LAC) (INPE)
%B Evolutionary Computation in Combinatorial Optimization Lecture Notes in Computer Science
%@versiontype publisher
%P 166-176
%4 sid.inpe.br/marciana/2005/07.05.12.38
%@documentstage not transferred
%D 2005
%V 3448
%O 5th European Conference on Evolutionary Computation in Combinatorial Optimization. Lausanne, MAR 30-APR 01, 2005
%A Oliveira,  A. C. M.,
%A Lorena, Luiz Aantonio Nogueira,
%@dissemination WEBSCI
%@area COMP