Abstract
An important reason for the continued popularity of
Artificial Neural Networks (ANNs) in the machine
learning community is that the gradient-descent
backpropagation procedure gives ANNs a locally optimal
change procedure and, in addition, a framework for
understanding the ANN learning performance. Genetic
programming (GP) is also a successful evolutionary
learning technique that provides powerful parameterized
primitive constructs. Unlike ANNs, though, GP does not
have such a principled procedure for changing parts of
the learned system based on its current performance.
This paper introduces Neural Programming, a
connectionist representation for evolving programs that
maintains the benefits of GP. The connectionist model
of Neural Programming allows for a regression
credit-blame procedure in an evolutionary learning
system. We describe a general method for an informed
feedback mechanism for Neural Programming, Internal
Reinforcement. We introduce an Internal Reinforcement
procedure and demonstrate its use through an
illustrative experiment.
Users
Please
log in to take part in the discussion (add own reviews or comments).