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- W4327767977 abstract "The goal of this paper is to use the genetic algorithm with adapted stopping criterion to reduce the computational cost of optimizing hyperparameters and network architecture in convolutional neural networks (CNNs) and to apply it on baseband modulation classification. Hyperparameter optimization is the maximization of a neural network's performance using a set of input network parameters and network architecture. With much research going into neural networks (e.g., CNNs), most research uses manually chosen hyperparameters instead of choosing optimal hyperparameters. Much work has gone into optimizing hyperparameters as they greatly effect the accuracy of CNNs and enable automated machine learning. Choosing the optimal hyperparameters can be done by applying the genetic algorithm to search for them. Each search operation requires a full training using a set of hyperparameters which is computationally expensive. Research has shown advantages to applying a stopping criterion to improve the neural network accuracy while reducing the computational cost. A stopping criterion is a training constraint to stop learning after a certain goal is achieved. Using the genetic algorithm with adapted stopping criterion, a fast CNN auto-generator has been designed, which can simultaneously generate a network of layers and their parameters from a pool of many potential networks that might not have been considered. Such an optimized CNN auto-generator has been applied on automatic modulation classification (AMC) through exploring the dataset DeepSig RADIOML 2016.10A. Compared to the literature, our design can achieve greater accuracy and lower run time. It also provides human designed CNN levels of accuracy on the RADIOML dataset while starting from no network but generating an entirely custom network for the dataset using a small subset in the CNN generation process." @default.
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- W4327767977 date "2023-01-08" @default.
- W4327767977 modified "2023-09-24" @default.
- W4327767977 title "Optimized CNN Auto-Generator Using GA With Stopping Criterion: Design and a Use Case" @default.
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- W4327767977 doi "https://doi.org/10.1109/ccnc51644.2023.10060860" @default.
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