A hybrid genetic algorithm for the identification of metabolic models

Genetic algorithms (GA) have been demonstrated to be a promising search and optimization technique that is more likely to converge to a global optimum than most alternative techniques. In an attempt to apply GA to estimate parameters of a metabolic model, however, we found that the slow convergence rate of GA becomes a major problem for its applications to model identification of dynamic systems due to the high computational costs associated with the evaluation of models. To alleviate this difficulty, we developed a hybrid approach that combines Nelder and Mead´s (1965) simplex method with the genetic algorithm. The hybrid approach not only speeds up GA´s rate of convergence, but also improves the quality of the solution found by pure GA