  Neuroevolution has greatly promoted Deep Neural Network (DNN) architecture
design and its applications, while there is a lack of methods available across
different DNN types concerning both their scale and performance. In this study,
we propose a self-adaptive neuroevolution (SANE) approach to automatically
construct various lightweight DNN architectures for different tasks. One of the
key settings in SANE is the search space defined by cells and organs
self-adapted to different DNN types. Based on this search space, a constructive
evolution strategy with uniform evolution settings and operations is designed
to grow DNN architectures gradually. SANE is able to self-adaptively adjust
evolution exploration and exploitation to improve search efficiency. Moreover,
a speciation scheme is developed to protect evolution from early convergence by
restricting selection competition within species. To evaluate SANE, we carry
out neuroevolution experiments to generate different DNN architectures
including convolutional neural network, generative adversarial network and long
short-term memory. The results illustrate that the obtained DNN architectures
could have smaller scale with similar performance compared to existing DNN
architectures. Our proposed SANE provides an efficient approach to
self-adaptively search DNN architectures across different types.
