@inproceedings{4a77e607d59b4ebca4631b734c5ed4ea,
title = "Probabilistic grammar-based deep neuroevolution",
abstract = "Designing deep neural networks by human engineers can be challenging because there are various choices of deep neural network structures. We developed a deep neuroevolution system to automatically design deep neural network structures using deep neuroevolution. Our approach defines a set of structures using a probabilistic grammar and searches for best network structures using Probabilistic Model Building Genetic Programming. Our approach takes advantage of the probabilistic dependencies found among the structures of networks. The system was applied to tackle the problem of the physiological signal classification of abnormal heart rhythm. In the classification problem, our discovered model is more accurate than AlexNet. Our discovered model uses about 2% of the total amount of parameters of AlexNet.",
keywords = "Estimation of Distribution Programming, Deep Neural Network",
author = "Pak-kan WONG and Man-leung WONG and Kwong-sak LEUNG",
year = "2019",
month = jul,
day = "13",
doi = "10.1145/3319619.3326778",
language = "English",
isbn = "9781450367486",
series = "GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion",
publisher = "Association for Computing Machinery, Inc",
pages = "87--88",
booktitle = "GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion",
note = "2019 Genetic and Evolutionary Computation Conference, GECCO 2019, GECCO 19 ; Conference date: 13-07-2019 Through 17-07-2019",
}