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RESEARCH PRODUCT

A hybrid genetic algorithm with local search

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Abstract A hybrid genetic algorithm with internal local search was developed for optimisations involving continuous variables. The reproduction probabilities were enhanced using the fitness values obtained when a local method was applied to each individual in the population. These estimations are more realistic, since consider not the apparent but the hidden, latent quality of each individual. The information gathered in the local search was also used to build an auxiliary population recording the successfully enhanced individuals, which allowed to detect the convergence and self-adapt the search limits. The size of this auxiliary population was kept constant by a cluster analysis strategy. The method was applied to the simultaneous deconvolution of sets of chromatograms monitored at a single detection wavelength, sharing two compounds (sulphapyridine and sulphisoxazole) at different concentration ratios. The results were compared with a classical genetic algorithm and a hybrid Powell–Gauss–Newton method, to check the benefits of the strategy. The method, called LOGA (locally optimised genetic algorithm) was superior in terms of the obtained residuals, and was able to retrieve the expected individual peak profiles with very low errors.