%0 Journal Article %T Identifica£¿£¿o gen¨¦tica de modelos por p¨®los e zeros baseada no compromisso entre os erros de polariza£¿£¿o e variancia %A Arruda %A L.V.R. %A F¨¢varo %A S¨ªlvio %A Neves-Jr. %A F. %J Sba: Controle & Automa£¿£¿o Sociedade Brasileira de Automatica %D 2003 %I Sociedade Brasileira de Autom¨¢tica %R 10.1590/S0103-17592003000200001 %X this work proposes a genetic algorithm (ga) to solve process estimation problems when the real process presents high orders polynomials (complexity model) or non-linearities, non-minimum phase behavior, etc. the algorithm finds the best linear model in the pole and zero form to represent the real plant using its input and output signals. a new chromosome representation was introduced and a new ''fitness'' function based on the tradeoff bias x variance was developed. to validate this genetic estimator, simulations studies were done and the ga performance was compared with one obtained by use of the traditional least square estimation method. %K identification %K least square estimation %K genetic algorithm %K reduced order model. %U http://www.scielo.br/scielo.php?script=sci_abstract&pid=S0103-17592003000200001&lng=en&nrm=iso&tlng=en