  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.
