Using a genetic algorithm to select parameters for a neural network that predicts aflatoxin contamination in peanuts

Aflatoxin contamination in crops of peanuts is a problem of significant health and financial importance, so it would be useful to develop techniques to predict the levels prior to harvest. Backpropagation neural networks have been used in the past to model problems of this type, however development of networks poses the complex problem of setting values for architectural features and backpropagation parameters. Genetic algorithms have been used in prior efforts to locate parameters for backpropagation neural networks. This paper describes the development of a genetic algorithm/backpropagation neural network hybrid (GA/BPN) in which a genetic algorithm is used to find architectures and backpropagation parameter values simultaneously for a backpropagation neural network that predicts aflatoxin contamination levels in peanuts based on environmental data.