Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Engineering Applications of Artificial Intelligence
Genetic algorithms based parameter identification of yeast fed-batch cultivation
NMA'10 Proceedings of the 7th international conference on Numerical methods and applications
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Simple genetic algorithms have been investigated aiming to improve the algorithm convergence time. Because of the stochastic nature of genetic algorithms, several runs have to be performed in order to achieve representative results. A procedure for purposeful genesis concerning intervals of variations of model parameters is proposed for a standard simple genetic algorithm, aiming to improve significantly the algorithm effectiveness. Such a stepwise methodology is applied to parameter identification of fed-batch cultivation of S. cerevisiae. The procedure is further validated to a modified simple genetic algorithm with changed sequence of main genetic algorithm operators, namely mutation, crossover and selection, proven to be faster than the standard one. Results obtained from both applications show significant improvement of the algorithm convergence time while saving the model accuracy.