自然与培育：环境资源在进化深度智能中的作用
Nature vs. Nurture: The Role of Environmental Resources in Evolutionary Deep Intelligence
摘要：
进化的深度智能在连续几代中合成高效的深度神经网络架构。受先天与后天争论的启发，我们提出了一项研究，通过改变模拟环境资源的可用性来研究外部因素对网络合成过程的作用。通过无性进化合成（1-parent）和有性进化合成（2-parent，3-parent和5-parent）使用MNIST数据集的10%子集合成的网络获得了实验结果。结果表明，较低的环境因素模型会导致性能准确性的逐渐下降和存储大小的减少。这可能会大大减少存储大小，性能准确性下降最小甚至没有下降，并且使用最低的环境因素模型合成最佳网络。
英文摘要：
Evolutionary deep intelligence synthesizes highly efficient deep neural networks architectures over successive generations. Inspired by the nature versus nurture debate, we propose a study to examine the role of external factors on the network synthesis process by varying the availability of simulated environmental resources. Experimental results were obtained for networks synthesized via asexual evolutionary synthesis (1-parent) and sexual evolutionary synthesis (2-parent, 3-parent, and 5-parent) using a 10% subset of the MNIST dataset. Results show that a lower environmental factor model resulted in a more gradual loss in performance accuracy and decrease in storage size. This potentially allows significantly reduced storage size with minimal to no drop in performance accuracy, and the best networks were synthesized using the lowest environmental factor models.
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