Optimization of Multilayer Perceptron Training Parameters Using Artificial Bee Colony and Genetic Algorithm
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In this paper, the momentum coefficient, learning rate, and the number of hidden neurons where the multilayer perceptron works best, are determined. The network and optimization algorithms are written in MATLAB, which was also successfully used to carry out results. To obtain the results, IRIS, mammographic_mass, and new_thyroid data sets have been used. Obtained results show that the determining effect on the neural learning process of parameters (momentum coefficient, learning rate, number of hidden neurons) are compatible with other approaches available in the literature. Both genetic algorithm (GA) and artificial bee colony (ABC) algorithm were successful on finding the values to get high performance as well as effect on performance of the population number.
KeywordsMultilayer perceptron, artificial bee colony algorithm, genetic algorithm, training parameters optimization
Document typePeer reviewed
Document versionFinal PDF
SourceProceedings of the 21st Conference STUDENT EEICT 2015. s. 338-340. ISBN 978-80-214-5148-3
- Student EEICT 2015