The Importance of Topology Evolution in NeuroEvolution: A Case Study Using Cartesian Genetic Programming of Artificial Neural Networks
نویسندگان
چکیده
NeuroEvolution (NE) is the application of evolutionary algorithms to Artificial Neural Networks (ANN). This paper reports on an investigation into the relative importance of weight evolution and topology evolution when training ANN using NE. This investigation used the NE technique Cartesian Genetic Programming of Artificial Neural Networks (CGPANN). The results presented show that the choice of topology has a dramatic impact on the effectiveness of NE when only evolving weights; an issue not facedwhenmanipulating bothweights and topology. This paper also presents the surprising result that topology evolution alone is far more effective when training ANN than weight evolution alone. This is a significant result as many methods which train ANN manipulate only weights.
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