Research Area: Machine Learning
The success of deep learning depends on finding an architecture to fit the task. As deep learning has scaled up to more challenging tasks, the architectures have become difficult to design by hand. This paper proposes an automated method, CoDeepNEAT, for optimizing deep learning architectures through evolution. By extending existing neuroevolution methods to topology, components, and hyperparameters, this method achieves results comparable to best human designs in standard benchmarks in object recognition and language modeling. It also supports building a real-world application of automated image captioning on a magazine website. Given the anticipated increases in available computing power, evolution of deep networks is promising approach to constructing deep learning applications in the future.
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Author(s) Name: Risto Miikkulainen,Jason Liang,Elliot Meyerson,Aditya Rawal,Daniel Fink,Olivier Francon,Bala Raju,Hormoz Shahrzad,Arshak Navruzyan,Nigel Duffy,Babak Hodjat
Journal name: Artificial Intelligence in the Age of Neural Networks and Brain Computing
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Publisher name: ELSEVIER
DOI: 10.1016/B978-0-12-815480-9.00015-3
Volume Information: 2019, Pages 293-312
