Neural networks analysis of steel plate processing augmented by multi-objective genetic algorithms

An earlier neural network analysis of processing of steel plates through hot rolling was subjected to a further refined analysis through some flexible neural networks that evolved using a multi-objective predator-prey genetic algorithm. The original data set expressing the Yield Strength and Ultimate Tensile Strength of the rolled slabs in terms of a total of 108 process variables were subjected to a systematic pruning through this evolutionary approach, till the nitrogen content of the steel emerged as the most significant input variable. A theoretical explanation is provided for this slightly unexpected result.