, C. Hervas Martnez, J. Mu~noz Perez

Abstract. Most processes in the real world are controlled by nonlinear models. This explains the interest of the scientic community in the development of new methods to estimate the parameters of nonlinear models that allow the modelling of such processes. In this article we propose a new method for the estimation of parameters for nonlinear problems using genetic algorithms with real encoding (RCGA). In these genetic algorithms we use a crossover operator based on condence intervals, which uses infor-mation from the best individuals in the population. For the resolution of this kind of problems, this operator is remarkably robust and eÆcient when compared with other crossover operators used in RCGAs.