Abstract
This paper compares the efficiency of two encoding
schemes for Artificial Neural Networks optimised by
evolutionary algorithms. Direct Encoding encodes the
weights for an a priori fixed neural network
architecture. Cellular Encoding encodes both weights
and the architecture of the neural network. In previous
studies, Direct Encoding and Cellular Encoding have
been used to create neural networks for balancing 1 and
2 poles attached to a cart on a fixed track. The poles
are balanced...
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