Study of How the Integration of Artificial Neural Network and Genetic Algorithm Should Be Made for Modeling Meteorological Data

Abstract

Artificial Neural Networks (ANN) have been widely used to model several types of data. The precision of ANN models is dependent upon their configuration, i.e., input parameters, training algorithm and architecture configurations. The problem lies in the amount of possible combinations of these parameters which results in countless unique ANNs. One method of finding a good combination of ANN parameters is to use a Genetic Algorithm (GA). Several studies combine a GA with an ANN to solve problems, however, it is not clear which parameters of an ANN the GA should determine. This work performed thousands of tests to verify the best combinations of parameters to use in integrations between GA and ANN especially in modeling meteorological data. Results have shown that the best approach is to use GA to define the input variables, activation function and the number of neurons of the ANN. Other tests showed that this same combination had similar results with different types of data indicating that this work can perhaps be applied to several types of problems.