papers available, hardcopies only.
 
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GENERAL ASYMMETRIC NEURAL NETWORKS AND
STRUCTURE DESIGN BY GENETIC ALGORITHMS
 
Stefan Bornholdt
Deutsches Elektronen-Synchrotron DESY, Notkestr. 85, 2000 Hamburg 52
 
Dirk Graudenz
Institut f\"ur Theoretische Physik, Lehrstuhl E, RWTH 5100 Aachen,
Germany.
 
A learning algorithm for neural networks based on genetic algorithms is
proposed. The concept leads in a natural way to a model for the
explanation of inherited behavior. Explicitly we study a simplified
model for a brain with sensory and motor neurons. We use a general
asymmetric network whose structure is solely determined by an
evolutionary process. This system is simulated numerically.
It turns out that the network obtained by the algorithm
reaches a stable state after a small number of sweeps.
Some results illustrating the learning capabilities are presented.
 
[to appear in Neural Networks]
 
preprints available from:
Stefan Bornholdt, DESY-T, Notkestr. 85, 2000 Hamburg 52, Germany.
Email: [log in to unmask] (hardcopies only, all rights reserved)
 
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