Evolutionary Optimization of Hyperparameters in Deep Learning Models
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- @InProceedings{Kim:2019:CEC,
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author = "Jin-Young Kim and Sung-Bae Cho",
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title = "Evolutionary Optimization of Hyperparameters in Deep
Learning Models",
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booktitle = "2019 IEEE Congress on Evolutionary Computation (CEC)",
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year = "2019",
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pages = "831--837",
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month = jun,
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/CEC.2019.8790354",
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abstract = "Recently, deep learning is one of the most popular
techniques in artificial intelligence. However, to
construct a deep learning model, various components
must be set up, including activation functions,
optimization methods, a configuration of model
structure called hyperparameters. As they affect the
performance of deep learning, researchers are working
hard to find optimal hyperparameters when solving
problems with deep learning. Activation function and
optimization technique play a crucial role in the
forward and backward processes of model learning, but
they are set up in a heuristic way. The previous
studies have been conducted to optimize either
activation function or optimization technique, while
the relationship between them is neglected to search
them at the same time. In this paper, we propose a
novel method based on genetic programming to
simultaneously find the optimal activation functions
and optimization techniques. In genetic programming,
each individual is composed of two chromosomes, one for
the activation function and the other for the
optimization technique. To calculate the fitness of one
individual, we construct a neural network with the
activation function and optimization technique that the
individual represents. The deep learning model found
through our method has 82.5percent and 53.0percent of
accuracies for the CIFAR-10 and CIFAR-100 datasets,
which outperforms the conventional methods. Moreover,
we analyze the activation function found and confirm
the usefulness of the proposed method.",
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notes = "Also known as \cite{8790354}",
- }
Genetic Programming entries for
Jin-Young Kim
Sung Bae Cho
Citations