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Function approximation is the problem of finding a system that best explains the relationship between input variables and an output variable. We propose two hybrid genetic algorithms (GAs) of parametric and nonparametric models for function approximation. The former GA is a genetic nonlinear Levenberg-Marquardt algorithm of parametric model. We designed the chromosomes in a way that geographically exploits the relationships between parameters. The latter one is another GA of nonparametric model that is combined with a feedforward neural network. The neuro-genetic hybrid here differs from others in that it evolves diverse input features instead of connection weights. We tested the two GAs with the problem of finding a better critical heat flux (CHF) function of nuclear fuel bundle which is directly related to the nuclear-reactor thermal margin and operation. The experimental result improved the existing CHF function originated from the KRB-1 CHF correlation at the Korea Atomic Energy Research Institute (KAERI) and achieved the correlation uncertainty reduction of 15.4% that would notably contribute to increasing the thermal margin of the nuclear power plants.