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.

翻译：神经革命极大地促进了深神经网络架构的设计及其应用,尽管在规模和性能方面缺乏不同DNN型不同类型现有方法,但缺乏不同DNN型建筑的规模和性能方面的可用方法。在本研究中,我们提议了一种自我适应性神经进化(SANE)方法,用于为不同任务自动建造各种轻量DNN型建筑。SANE的关键设置之一是由细胞和器官自行适应不同的DNNN型类型所定义的搜索空间。在这个搜索空间的基础上,设计了一个具有统一进化设置和操作的建设性进化战略,旨在逐步发展DNNN型建筑。SANE能够自我调整进化探索和开发,以提高搜索效率。此外,我们制定了一种观察性计划,通过限制物种内部的选择竞争来保护进化的早期趋同。为了评估SANE,我们进行了神经进化实验,以产生不同的DNNN型建筑,包括革命神经网络、基因反应网络和长期的短期记忆。结果显示,获得的DNNNE型建筑的规模可能小于现有的DNNN型搜索结构。我们提议的SAN型结构提供了一种高效的自我搜索。