Genetic Operators Affect on ANN of Runoff Forecast

Genetic algorithms (GA) can effect the artificial neural network(ANN) runoff forecast accuracy by optimizing the ANN initial weights and biases. In this paper, the numerical experiment was designed by the uniform design to compose the difference in runoff forecast accuracy between various combinations of genetic operator. The results show that data-in model is little effect to the individual distributing in final population while the different genetic operator combination is much more effect. Regression estimation indicates the genetic operator that effect main is different at the two data-in model; there is great difference to runoff forecast accuracy among various combinations of genetic operators to optimize the ANN the initial weights and biases, and the optimizing result from ANN which no scale the data to $$0,1$$ is better than that from ANN scaling, there is no effect superpose to runoff forecast accuracy between scaling data-in and optimizing ANN initial weights and biases by GA.