Abstract
The evolution of hardware has enabled Artificial Neural Networks to become a staple solution to many modern Artificial Intelligence problems such as natural language processing and computer vision. The neural network’s effectiveness is highly dependent on the optimizer used during training, which motivated significant research into the design of neural network optimizers. Current research focuses on creating optimizers that perform well across different topologies and network types. While there is evidence that it is desirable to fine-tune optimizer parameters for specific networks, the benefits of designing optimizers specialized for single networks remain mostly unexplored.
In this paper, we propose an evolutionary framework called Adaptive AutoLR (ALR) to evolve adaptive optimizers for specific neural networks in an image classification task. The evolved optimizers are then compared with state-of-the-art, human-made optimizers on two popular image classification problems. The results show that some evolved optimizers perform competitively in both tasks, even achieving the best average test accuracy in one dataset. An analysis of the best evolved optimizer also reveals that it functions differently from human-made approaches. The results suggest ALR can evolve novel, high-quality optimizers motivating further research and applications of the framework.
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Acknowledgments
This work is partially funded by: Fundação para a Ciência e Tecnologia (FCT), Portugal, under the grant UI/BD/151053/2021, and by national funds through the FCT - Foundation for Science and Technology, I.P., within the scope of the project CISUC - UID/CEC/00326/2020 and by European Social Fund, through the Regional Operational Program Centro 2020.
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Carvalho, P., Lourenço, N., Machado, P. (2022). Evolving Adaptive Neural Network Optimizers for Image Classification. In: Medvet, E., Pappa, G., Xue, B. (eds) Genetic Programming. EuroGP 2022. Lecture Notes in Computer Science, vol 13223. Springer, Cham. https://doi.org/10.1007/978-3-031-02056-8_1
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DOI: https://doi.org/10.1007/978-3-031-02056-8_1
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