In recent years, the CNN architectures designed by evolution algorithms have proven to be competitive with handcrafted architectures designed by experts. However, these algorithms need a lot of computational power, which is beyond the capabilities of most researchers and engineers. To overcome this problem, we propose an evolution architecture under length constraints. It consists of two algorithms: a search length strategy to find an optimal space and a search architecture strategy based on genetic algorithm to find the best individual in the optimal space. Our algorithms reduce drastically resource cost and also keep good performance. On the Cifar-10 dataset, our framework presents outstanding performance with an error rate of 5.12% and only 4.6 GPU a day to converge to the optimal individual -22 GPU a day less than the lowest cost automatic evolutionary algorithm in the peer competition.

翻译：近年来，基于进化算法设计的CNN架构已被证明能与专家手工设计的架构相媲美。然而，这类算法需要大量计算资源，超出了大多数研究人员和工程师的能力范围。为解决这一问题，我们提出了一种长度约束下的进化架构方法。该方法包含两种算法：用于寻找最优空间的搜索长度策略，以及基于遗传算法在最优空间中寻找最优个体的搜索架构策略。我们的算法大幅降低了资源消耗，同时保持了良好的性能。在Cifar-10数据集上，我们的框架展现出卓越的性能，错误率仅为5.12%，且仅需每天4.6个GPU即可收敛到最优个体——比现有同类方法中成本最低的自动进化算法每天少用22个GPU。