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A comparison of fitting growth models with a genetic algorithm and nonlinear regression
1USDA/ARS, South Central Poultry Research Laboratory, Mississippi State, Mississippi 39762, USA. broush@msamsstate.ars.usda.gov
Poultry Science
|March 24, 2005
Summary
Genetic algorithms (GA) and nonlinear regression equally fit poultry growth models. The study found the growth equation
Area of Science:
- * Poultry Science
- * Computational Biology
- * Mathematical Modeling
Background:
- * Poultry growth modeling is crucial for optimizing production.
- * Traditional nonlinear regression methods require coefficient estimates and derivatives.
- * Genetic algorithms (GA) offer an alternative optimization approach based on evolutionary principles.
Purpose of the Study:
- * To compare the efficacy of genetic algorithms (GA) versus nonlinear regression in fitting poultry growth models.
- * To evaluate if GA's nonlinear approach provides a better fit for growth equation coefficients.
- * To identify advantages and disadvantages of each method for parameter estimation.
Main Methods:
- * Two poultry growth datasets (male broiler BW) were analyzed.
- * Growth data were fitted to logistic, Gompertz, Gompertz-Laird, and saturated kinetic models.
- * Models were fitted using SAS nonlinear algorithm (NLIN) and a genetic algorithm (GA).
Main Results:
- * No statistical differences were found in the residuals between GA and nonlinear regression fits.
- * Both methods produced residuals with oscillations, indicating limitations in the growth models themselves.
- * Genetic algorithms successfully determined growth equation coefficients but were slower to converge.
Conclusions:
- * The methodology (GA vs. nonlinear regression) did not significantly impact growth equation fitting.
- * The primary limitation in fitting poultry growth data lies in the chosen equation forms, not the fitting algorithm.
- * Genetic algorithms offer flexibility in parameter specification (ranges vs. estimates) and potential for global optimum seeking.
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