Application of evolutionary algorithms to optimise one- and two-dimensional gradient chromatographic separations

•Have assessed use of evolutionary algorithms in 1D- and 2D-LC method development.•Have compared 3 algorithm classes and benchmarked them against plain grid search.•The optimised evolution strategies (ES) algorithm is identified as the best performing.•Difference between algorithms grows with difficulty of the separation problem. We report on the performance of three classes of evolutionary algorithms (genetic algorithms (GA), evolution strategies (ES) and covariance matrix adaptation evolution strategy (CMA-ES)) as a means to enhance searches in the method development spaces of 1D- and 2D-chromatography. After optimisation of the design parameters of the different algorithms, they were benchmarked against the performance of a plain grid search. It was found that all three classes significantly outperform the plain grid search, especially in terms of the number of search runs needed to achieve a given separation quality. As soon as more than 100 search runs are needed, the ES algorithm clearly outperforms the GA and CMA-ES algorithms, with the latter performing very well for short searches (