In the present work the artificial neural network linked genetic algorithm was applied for the optimization of fermentation media components like carbon and nitrogen sources for L-asparaginase production by Enterobacter aerogenes MTCC 2823 in submerged fermentation. Artificial neural network (ANN) based back propagation algorithm was used to train and test the neural network using the experimental activity obtained by central composite design. Higher value of coefficient of determination (R2=0.984) of artificial neural network justified an excellent correlation between the media components and L-asparaginase activity, the artificial neural network model fitted well with high statistical reliability and significance than RSM model (R2=0.871) developed by central composite design. The predicted optimum concentration of the media components using artificial neural network linked genetic algorithm was sodium citrate 2.09%, DAHP 0.25% and L-asparagine 0.92% with the maximum predicted L-asparaginase activity of 18.59 IU/mL which was close to the experimental L-asparaginase activity of 18.72 IU/mL at simulated optimum conditions.

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Over the recent decades, the amount of data generated has been growing exponentially, the existing machine learning algorithms are not feasible for processing of such huge amount of data. To solve such kind of issues, we have two commonly adopted schemes, one is scaling up the data mining algorithms and other one is data reduction. Scaling up the data mining algorithms is not a best way, but data reduction is fairly possible. In this paper, cuttlefish optimisation algorithm along with tabu search approach is used for data reduction. Dataset can be reduced mainly in two ways, one is the selecting optimal subset of features from the original dataset, in other words eliminating those features which are contributing lesser information another method is selecting optimal subset of instances from the original data set, in other words eliminating those instances which are contributing lesser information. Cuttlefish optimisation algorithm with tabu search finds both optimal subset of features and instances. Optimal subset of feature and instance obtained from the cuttlefish algorithm with tabu search provides a similar detection rate, accuracy rate, lesser false positive rate and the lesser computational time for training the classifier that we obtained from the original data set.

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