A Comparison of Fitting Growth Models with a Genetic Algorithm and Nonlinear Regression

ABSTRACT A genetic algorithm (GA), an optimization procedure based on the theory of evolution, was com-pared with nonlinear regression for the ability of the 2 algorithms to fit the coefficients of poultry growth mod-els. It was hypothesized that the nonlinear approach of using GA to define the parameters of growth equations would better fit the growth equations than the use of nonlinear regression. Two sets of growth data from the literature, consisting of male broiler BW grown for 168 and 170 d, were used in the study. The growth data were fit to 2 forms of the logistic model, the Gompertz, the Gompertz-Laird, and the saturated kinetic models using the SAS nonlinear algorithm (NLIN) procedure and a GA. There were no statistical differences for the comparison of the residuals (the difference between observed and predicted BWs) of growthmodels fit by a GA or nonlinear regression. The plotted residuals for the nonlinear regres-sion and GA-determined growth values confirmed obser