  The goals of this research were to search for Convolutional Neural Network
(CNN) architectures, suitable for an on-device processor with limited computing
resources, performing at substantially lower Network Architecture Search (NAS)
costs. A new algorithm entitled an Early Exit Population Initialisation (EE-PI)
for Evolutionary Algorithm (EA) was developed to achieve both goals. The EE-PI
reduces the total number of parameters in the search process by filtering the
models with fewer parameters than the maximum threshold. It will look for a new
model to replace those models with parameters more than the threshold. Thereby,
reducing the number of parameters, memory usage for model storage and
processing time while maintaining the same performance or accuracy. The search
time was reduced to 0.52 GPU day. This is a huge and significant achievement
compared to the NAS of 4 GPU days achieved using NSGA-Net, 3,150 GPU days by
the AmoebaNet model, and the 2,000 GPU days by the NASNet model. As well, Early
Exit Evolutionary Algorithm networks (EEEA-Nets) yield network architectures
with minimal error and computational cost suitable for a given dataset as a
class of network algorithms. Using EEEA-Net on CIFAR-10, CIFAR-100, and
ImageNet datasets, our experiments showed that EEEA-Net achieved the lowest
error rate among state-of-the-art NAS models, with 2.46% for CIFAR-10, 15.02%
for CIFAR-100, and 23.8% for ImageNet dataset. Further, we implemented this
image recognition architecture for other tasks, such as object detection,
semantic segmentation, and keypoint detection tasks, and, in our experiments,
EEEA-Net-C2 outperformed MobileNet-V3 on all of these various tasks. (The
algorithm code is available at https://github.com/chakkritte/EEEA-Net).
