Data mining using a genetic algorithm-trained neural network

Neural Networks have been shown to perform well for mapping unknown functions from historical data in many business areas, such as Accounting, Finance, and Management. Although there have been many successful applications of neural networks in business, additional information about the networks is still lacking, specifically, determination of inputs that are relevant to the neural network model. It is apparent that by knowing which inputs are actually contributing to model prediction a researcher has gained additional knowledge about the problem itself. This can lead to a parsimonious neural network architecture, better generalization for out-of-sample prediction, and probably the most important, a better understanding of the problem. It is shown in this paper that by using a modified genetic algorithm for neural network training, relevant inputs can be determined while simultaneously searching for a global solution.