Impacts of Darwinian Evolution on Pre-trained Deep Neural Networks

التفاصيل البيبلوغرافية
العنوان: Impacts of Darwinian Evolution on Pre-trained Deep Neural Networks
المؤلفون: Du, Guodong, Jiang, Runhua, Yang, Senqiao, Li, Haoyang, Chen, Wei, Li, Keren, Goh, Sim Kuan, Tang, Ho-Kin
سنة النشر: 2024
المجموعة: Computer Science
مصطلحات موضوعية: Computer Science - Neural and Evolutionary Computing, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
الوصف: Darwinian evolution of the biological brain is documented through multiple lines of evidence, although the modes of evolutionary changes remain unclear. Drawing inspiration from the evolved neural systems (e.g., visual cortex), deep learning models have demonstrated superior performance in visual tasks, among others. While the success of training deep neural networks has been relying on back-propagation (BP) and its variants to learn representations from data, BP does not incorporate the evolutionary processes that govern biological neural systems. This work proposes a neural network optimization framework based on evolutionary theory. Specifically, BP-trained deep neural networks for visual recognition tasks obtained from the ending epochs are considered the primordial ancestors (initial population). Subsequently, the population evolved with differential evolution. Extensive experiments are carried out to examine the relationships between Darwinian evolution and neural network optimization, including the correspondence between datasets, environment, models, and living species. The empirical results show that the proposed framework has positive impacts on the network, with reduced over-fitting and an order of magnitude lower time complexity compared to BP. Moreover, the experiments show that the proposed framework performs well on deep neural networks and big datasets.
Comment: This work has been submitted to the IEEE for possible publication
نوع الوثيقة: Working Paper
URL الوصول: http://arxiv.org/abs/2408.05563
رقم الأكسشن: edsarx.2408.05563
قاعدة البيانات: arXiv