SPENSER: Towards a NeuroEvolutionary Approach for Convolutional Spiking Neural Networks

Created by W.Langdon from gp-bibliography.bib Revision:1.7662

@InProceedings{branquinho:2023:NEWK,

• author = "Henrique Branquinho and Nuno Lourenco and Ernesto Costa",
• title = "{SPENSER:} Towards a {NeuroEvolutionary} Approach for Convolutional Spiking Neural Networks",
• booktitle = "Neuroevolution at work",
• year = "2023",
• editor = "Ernesto Tarantino and Edgar Galvan and Ivanoe {De Falco} and Antonio {Della Cioppa} and Scafuri Umberto and Mengjie Zhang",
• pages = "2115--2122",
• address = "Lisbon, Portugal",
• series = "GECCO '23",
• month = "15-19 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, grammatical evolution, neuroevolution, computer vision, spiking neural networks, DENSER",
• isbn13 = "9798400701191",
• DOI = "doi:10.1145/3583133.3596399",
• size = "8 pages",
• abstract = "Spiking Neural Networks (SNNs) have attracted recent interest due to their energy efficiency and biological plausibility. However, the performance of SNNs still lags behind traditional Artificial Neural Networks (ANNs), as there is no consensus on the best learning algorithm for SNNs. Best-performing SNNs are based on ANN to SNN conversion or learning with spike-based backpropagation through surrogate gradients. The focus of recent research has been on developing and testing different learning strategies, with hand-tailored architectures and parameter tuning. Neuroevolution (NE), has proven successful as a way to automatically design ANNs and tune parameters, but its applications to SNNs are still at an early stage. DENSER is a NE framework for the automatic design and parametrization of ANNs, based on the principles of Genetic Algorithms (GA) and Structured Grammatical Evolution (SGE). In this paper, we propose SPENSER, a NE framework for SNN generation based on DENSER, for image classification on the MNIST and Fashion-MNIST datasets. SPENSER generates competitive performing networks with a test accuracy of 99.42\% and 91.65\% respectively.",
• notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Henrique Branquinho Nuno Lourenco Ernesto Costa

Citations