Aflatoxin Prediction Using a GA Trained Neural Network

C. EHenderson W.D. Potter, R. W. McClendon, G. Hoogenboom

Predicting the level of aflatoxin contamination in crops of peanuts is a task of significant importance. Backpropagation neural networks have been used in the past to model this problem, but use of the backpropagation algorithm for training introduces limitations and difficulties. Therefore, it is useful to explore alternative learning algorithms. Genetic algorithms provide an effective technique for searching large spaces, and have been used in the past to train neural networks. This paper describes the development of a genetic algorithm/neural network hybrid in which a genetic algorithm is used to find weight assignments for a neural network that predicts aflatoxin contamination levels in peanuts based on environmental data.


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