Efficient Neural Network Pruning during Neuro-Evolution

A paper based on my student research project got published at the International Joint Conference on Neural Networks, IJCNN 2009. The conference is held jointly by the International Neural Network Society INNS and the IEEE Computational Intelligence Society – the two leading professional organizations for researchers working in neural networks. The paper can be found at the IEEE CS digital library. The abstract reads as follows:

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 network’s parameters, candidate connections for removal are identified without the need for extensive additional calculations.

Efficient Neural Network Pruning during Neuro-Evolution (PDF)

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