A comparison of fitting growth models with a genetic algorithm and nonlinear regression.
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Summary
The fitting of the growth equations was not so much a problem with the fitting methodology as it is with the form of the equation, and it was found that GA could successfully determine the coefficients of growth equations.
- Type
- article
- Published
- 2005-03-01
- Cited by
- 47
- References
- 12
- Access
- Open access
- OpenAlex
- https://openalex.org/W15782921
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:3603758
Keywords
Computer science
References
- Modeling turkey growth with the relative growth rate.
- Genetic algorithms + data structures = evolution programs (3rd ed.)
- Comparison of three nonlinear regression models for describing broiler growth curves.
- On the practical genetic algorithms
- An Algorithm for Least-Squares Estimation of Nonlinear Parameters
- Genetic Algorithms + Data Structures = Evolution Programs
- Comparison of three nonlinear and spline regression models for describing chicken growth curves.
- Genetic Algorithms in Search Optimization and Machine Learning
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- Seedling emergence of tall fescue and wheatgrass under different climate conditions in Iran
- Estimating time and temperature dependent yield stress of cement paste using oscillatory rheology and genetic algorithms
- An Extended Boltzmann Model for Forecasting Biogas Consumption in Rural China
- Comparison of fitting weed seedling emergence models with nonlinear regression and genetic algorithm
- Improved evaluation of cover crop species by growth and root factors
- Pullulan-sodium alginate based edible films: Rheological properties of film forming solutions
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