Efficient neural network pruning during neuro-evolution
In this article we present a new method for the pruning of unnecessary
connections from neural networks created by an evolutionary algorithm
(neuro-evolution). Pruning not only decreases the complexity of the
network but also improves the numerical stability of the parameter
optimisation process. We show results from experiments where
connection pruning is incorporated into EANT2, an evolutionary
reinforcement learning algorithm for both the topology and parameters
of neural networks. By analysing data from the evolutionary
optimisation process that determines the networks parameters,
candidate connections for removal are identified without the need for
extensive additional calculations.