Practical neural network recipes in C++
Practical neural network recipes in C++
A Racing Algorithm for Configuring Metaheuristics
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Rigorous Analyses for the Combination of Ant Colony Optimization and Local Search
ANTS '08 Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence
Introduction to Neural Networks for Java, 2nd Edition
Introduction to Neural Networks for Java, 2nd Edition
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An attempt was made to develop a computational model based on artificial neural network and ant colony optimization to estimate the composition of medium components for maximizing the productivity of Penicillin G Acylase (PGA) enzyme from Escherichia coli DH5@a strain harboring the plasmid pPROPAC. As a first step, an artificial neural network (ANN) model was developed to predict the PGA activity by considering the concentrations of seven important components of the medium. Design of experiments employing central composite design technique was used to obtain the training samples. In the second step, ant colony optimization technique for continuous domain was employed to maximize the PGA activity by finding the optimal inputs for the developed ANN model. Further, the effect of a combination of ant colony optimization for continuous domain with a preferential local search strategy was studied to analyze the performance. For a comparative study, the training samples were fed into the response surface methodology optimization software to maximize the PGA production. The obtained PGA activity (56.94U/mL) by the proposed approach was found to be higher than that of the obtained value (45.60U/mL) with the response surface methodology. The optimum solution obtained computationally was experimentally verified. The observed PGA activity (55.60U/mL) exhibited a close agreement with the model predictions.