Computer
The ant colony optimization meta-heuristic
New ideas in optimization
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A multipopulation genetic algorithm aimed at multimodal optimization
Advances in Engineering Software
A unified view on hybrid metaheuristics
HM'06 Proceedings of the Third international conference on Hybrid Metaheuristics
Improvement of genetic algorithm performance for identification of cultivation process models
EC'08 Proceedings of the 9th WSEAS International Conference on Evolutionary Computing
Parameter Estimation of a Monod-Type Model Based on Genetic Algorithms and Sensitivity Analysis
Large-Scale Scientific Computing
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This paper endeavors to show that genetic algorithms, namely Multipopulation genetic algorithms (MpGA), are of great utility in cases where complex cultivation process models have to be identified and, therefore, rational choices have to be made. A system of five ordinary differential equations is proposed to model biomass growth, glucose utilization and acetate formation. Parameter optimization is carried out using experimental data set from an E. coli cultivation. Several conventional algorithms for parameter identification (Gauss-Newton, Simplex Search and Steepest Descent) are compared to the MpGA. A general comment on this study is that traditional optimization methods are generally not universal and the most successful optimization algorithms on any particular domain, especially for the parameter optimization considered here. They have been fairly successful at solving problems of type which exhibit bad behavior like multimodal or nondifferentiable for more conventional based techniques.