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The number of instrumental parameters controlling modern analytical apparatus can be substantial, and varying them systematically to optimize a particular chromatographic separation, for example, is out of the question because of the astronomical number of combinations that are possible (i.e., the "search space" is very large). However, heuristic methods, such as those based on evolutionary computing, can be used to explore such search spaces efficiently. We here describe the implementation of an entirely automated (closed-loop) strategy for doing this and apply it to the optimization of gas chromatographic separations of the metabolomes of human serum and of yeast fermentation broths. Without human intervention, the Robot Chromatographer system (i) initializes the settings on the instrument, (ii) controls the analytical run, (iii) extracts the variables defining the analytical performance (specifically the number of peaks, signal/noise ratio, and run time), (iv) chooses (via the PESA-II multiobjective genetic algorithm), and (v) programs the next series of instrumental settings, the whole continuing in an iterative cycle until suitable sets of optimal conditions have been established. Genetic programming was used to remove noise peaks and to establish the basis for the improvements observed. The system showed that the number of peaks observable depended enormously on the conditions used and served to increase them by as much as 3-fold (e.g., to over 950 in human serum) while in many cases maintaining or reducing the run time and preserving excellent signal/noise ratios. The evolutionary closed-loop machine learning strategy we describe is generic to any type of analytical optimization.
O’Hagan et al. (Fri,) studied this question.