Genetic Algorithms and Neural Networks by Data Mining

Abstract

We propose, in our study, an approach of hybridization (Genetic Algorithms/Neural Networks) in order to apply it to solve our problems, namely “optimization of the budget estimates of the Aval activity of Sonatrach”. Indeed, within the downstream activity, the budget estimates of the expenditure are done every 5 years. Those must approach reality as much as possible in theory; therefore we took as bases training the table which contains the real expenditure (entries) 3 years previous. While studying the genetic algorithms and the networks of neurons separately, the idea came to us to use the genetic algorithms for the adjustment of the synaptic weights at the time of the phase of training of our network of neurons, and to use the capacity of prediction of the network of neurons to predict our budgetary expenditure. One starts from a population of more than 100 individuals (chromosomes), each one of them represents a configuration of weight, for the network of neurons, drawn by chance between [-1,1]. For each individual, one will calculate the average error between the calculated exits and the exits wished for all the examples of our base of training. The function fitness is anything else only this average error. One selects then the individuals ready to reproduce who are those which have the weakest error. One uses the operators of crossing and change to generate a new generation. This process will be repeated to L `obtaining of an individual whose error will be minimal. The configuration of weight represented by this individual will be to use for our prediction.