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GACC: Genetic Aided Cascade-Correlation

areas/genetic/ga/systems/gacc/
This directory contains Genetic Aided Cascade-Correlation (GACC). Genetic algorithms are applied to the optimization of the weights in the cascade-correlation learning architecture. In this architecture, the neural network starts out with a layer of output neurons. The output neuron weights are then adjusted to minimize the error in the network. A hidden neuron is then added and its weights adjusted so its output correlates with the error in the network. It is then connected to the output neurons and the weights of the output neuron are once again readjusted. This process of adding neurons continues until a network with an acceptable error is produced. Genetic algorithms are used to find the weights for both the hidden and output neurons. We attempt to use the global optimization characteristics of genetic algorithms to find the global set of weights. However, while simple genetic algorithms can find the area of the weight space where there is a minimum error for the output weights or maximum correlation for the hidden layers, they do not converge to the actual minimum or maximum. We must find a way to supplement the simple genetic algorithm. In our approach, the Genetic aided cascade-correlation, we explore the weight space first by using simple genetic algorithms with non-overlapping populations and binary encoded weights. We then use Quickprop to converge to the minimum or maximum. We have applied our algorithm to the two spiral test with resulting average network sizes of an average of 21.6 hidden nodes.
Version: 15-MAR-93 Requires: C CD-ROM: Prime Time Freeware for AI, Issue 1-1 Author(s): Erik Mayer Univ. of Toledo Keywords: Authors!Mayer, Cascade Correlation, GACC, Genetic Algorithms, Machine Learning, Neural Networks, Quickprop, Univ. of Toledo References: Mayer, E. "Genetic Aided Cascade-Correlation." COGANN Workshop ICGA-93, Champaign-Urbana, Illinois, July, 1993. Mayer, E. "Genetic Algorithm Approach to Neural Network Optimization." Masters Thesis, University of Toledo, Toledo, Ohio, August 1993. Cios, K.J., Mayer, E., Vary, A., and Kautz, H. "Neural Networks in Analysis of Acousto-ultrasonic Data." Second International Conference of Acousto-ultrasonics, Atlanta, Georgia, June 1993.
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