A new genetic algorithm (GA) using hybrid coding method to train the neural network model for predication of carbon content in arc furnace steel is proposed

that is first to use binary coding method then to use decimal coding method continuously to optimize and train the weighted threshold of predictive model. The hybrid coding method combines the advantages of binary coding method with strong search ability and decimal coding method with arbitrarily small variance. Simulation results show that the genetic algorithm (GA) using hybrid coding method has faster convergence rate and better search performance

for instance as steel grade with 0. 85% ~ 1. 00% C is melted by 100 t arc furnace

for ±0. 04% precision of predictive carbon content in steel the hit ratio by using hybrid coding method GA is 96%

but the hit ratio by using binary coding method GA is 90% .