Quantum-inspired feature and parameter optimization of evolving spiking neural networks with a case study from ecological modelling
Schliebs, S; Defoin-Platel, M; Worner, S; Kasabov, N
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The paper introduces a framework and implementation of an integrated connectionist system, where the features and the parameters of an evolving spiking neural network are optimised together using a quantum representation of the features and a quantum inspired evolutionary algorithm for optimisation. The proposed model is applied on ecological data modeling problem demonstrating a significantly better classification accuracy than traditional neural network approaches and a more appropriate feature subset selected from a larger initial number of features. Results are compared to a naive Bayesian classifier.