Evaluation of artificial neural networks for modelling and optimization of medium composition with a genetic algorithm

Abstract A new concept was evaluated for experimental multi-objective medium optimization using a genetic algorithm which is supported by an artificial neural network (ANN). The ANN is used to model objective functions with the medium components as variables each time a new data set has been produced. An appropriate topology of the ANN was first identified with simulation studies using a multi-dimensional test function (De Jong's function). The performance of this ANN model was validated from generation to generation with the data of an experimental optimization of a medium with 13 medium components for Synechococcus PCC 7942. Objective functions were the simultaneous maximization of biomass concentration and conversion of pentafluoroacetophenon (PFAP) for asymmetric synthesis of (S)-(−)1-(pentafluorophenyl)-ethanol. The mean absolute error of the ANN simulation was within the experimental estimation error after six from eight generations for one of the two objective functions (PFAP conversion). This artificial neural network supported genetic algorithm (ANNSGA) can thus be implemented at the end of a stochastic optimization procedure to reduce the experimental effort.

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