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The paper describes an evolutionary approach to artificial neural network (NN) training, which is used to determine the state of oil-production equipment. A new artificial NN weight coefficient coding method using multi- chromosomes is proposed. The genetic operators of crossingover and mutation applied to multi-chromosomes are examined. A genetic algorithm structure of artificial NN training based on the developed genetic operators is proposed. A comparison of the proposed approach to NN training with existing ones has been carried out.

The Application of Evolutionary Algorithms in the Artificial Neural Network Training Process for the Oilfield Equipment Malfunctions’ Forecasting