Neuroevolution with box mutation: An adaptive and modular framework for evolving deep neural networks

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Abstract

The pursuit of self-evolving neural networks has driven the emerging field of Evolutionary Deep Learning, which combines the strengths of Deep Learning and Evolutionary Computation. This work presents a novel method for evolving deep neural networks by adapting the principles of Geometric Semantic Genetic Programming, a subfield of Genetic Programming, and Semantic Learning Machine. Our approach integrates evolution seamlessly through natural selection with the optimization power of backpropagation in deep learning, enabling the incremental growth of neural networks’ neurons across generations. By evolving neural networks that achieve nearly 89% accuracy on the CIFAR-10 dataset with relatively few parameters, our method demonstrates remarkable efficiency, evolving in GPU minutes compared to the field standard of GPU days.
Original languageEnglish
Article number110767
Pages (from-to)1-15
Number of pages15
JournalApplied Soft Computing
Volume147
Issue numberNovember
DOIs
Publication statusPublished - 1 Nov 2023

Keywords

  • Neuroevolution
  • Evolutionary deep learning
  • Neural architecture search
  • Supervised learning

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