Abstract
This Paper presents the Architecture optimization of Neural Networks using parallel Genetic Algorithms for pattern recognition based on person faces. The optimization consists in obtaining the best architecture in layers, neurons per layer, and achieving the less recognition error in a shorter training time using parallel programming techniques to exploit the resources of a machine with a multi-core architecture. We show the obtained performance by comparing results of the training stage for sequential and parallel implementations.
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Cárdenas, M., Melin, P., Cruz, L. (2010). Parallel Genetic Algorithms for Architecture Optimization of Neural Networks for Pattern Recognition. In: Melin, P., Kacprzyk, J., Pedrycz, W. (eds) Soft Computing for Recognition Based on Biometrics. Studies in Computational Intelligence, vol 312. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15111-8_19
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DOI: https://doi.org/10.1007/978-3-642-15111-8_19
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