- author = "Marco Silva and Adriano Koshiyama and Marley Vellasco and Edson Cataldo",
- booktitle = "International Joint Conference on Neural Networks (IJCNN 2014)",
- title = "Evolutionary features and parameter optimization of spiking neural networks for unsupervised learning",
- year = "2014",
- month = jul,
- pages = "2391--2398",
- keywords = "genetic algorithms, genetic programming, SNN",
-
DOI = "
doi:10.1109/IJCNN.2014.6889566",
- abstract = "This paper introduces two new hybrid models for clustering problems in which the input features and parameters of a spiking neural network (SNN) are optimised using evolutionary algorithms. We used two novel evolutionary approaches, the quantum-inspired evolutionary algorithm (QIEA) and the optimisation by genetic programming (OGP) methods, to develop the quantum binary-real evolving SNN (QbrSNN) and the SNN optimised by genetic programming (SNN-OGP) neuro-evolutionary models, respectively. The proposed models are applied to 8 benchmark datasets, and a significantly higher clustering accuracy compared to a standard SNN without feature and parameter optimisation is achieved with fewer iterations. When comparing QbrSNN and SNN-OGP, the former performed slightly better but at the expense of increased computational effort.",
-
notes = "M. Silva, M.M.B.R. Vellasco, and A. Koshiyama are with
the Department of Electrical Engineering, Pontifical
Catholic University of Rio de Janeiro (PUC-Rio),
Brazil,
Also known as \cite{6889566}",
