In order to avoid local minimum points during training BP network

adaptive crossover mutation algorithm and elitist model algorithm were used

which could optimize the weight and bias of BP network so as to build a hybrid genetic algorithm neural network. The algorithm consisted of the respective advantages of adaptive crossover mutation algorithm and elitist model algorithm. The best solution of problems was obtained by using as few generations as possible.In this way

local minimum point was prevented and the better average fitness and individual of best fitness were obtained in the algorithm. The artificial neural network with the above optimization strategy could greatly improve stability and speed of convergence and guarantee convergence to a global minimum point.Application of artificial neural network to predict physical properties of materials is a promising field to be developed. In this paper the proposed artificial neural network based on hybrid genetic algorithm was used to predict melting points of organic compounds. The molecular mass

critical density

normal boiling point and dipole moment of organic compounds were used in prediction.The predicted values based on artificial neural network with hybrid genetic algorithm were better than the values calculated with the Joback equation and Xu equation.