On the use of genetic algorithms for response surface modeling in high-performance liquid chromatography and their combination with the Microsoft Solver

Four genetic algorithms--the classical, Haupt's, Brunetti's and a modification of the classical algorithm suggested in the present paper--are examined when they are used for the modeling of response surfaces in high-performance liquid chromatography (HPLC). We found that the best results are obtained from our modification and the worst by Haupt's algorithm. The classical genetic algorithm gives satisfactory results, better than those of Brunetti's algorithm. We also ascertained that all genetic algorithms may get stuck in a local minimum other than the global one, except for our modification, which can be considered to approach a global method. Finally, the time needed for the optimization of a genetic algorithm and the combination of a genetic algorithm with a non-linear least-squares routine are considered and discussed.