  Evolutionary algorithms (EAs) have emerged as a powerful framework for
optimization, especially for black-box optimization. This paper first focuses
on automated EA: Automated EA exploits structure in the problem of interest to
automatically generate update rules (optimization strategies) for generating
and selecting potential solutions so that it can move a random population near
the optimal solution. However, current EAs cannot achieve this goal due to the
poor representation of the optimization strategy and the weak interaction
between the optimization strategy and the target task. We design a deep
evolutionary convolution network (DECN) to realize the move from hand-designed
EAs to automated EAs without manual interventions. DECN has high adaptability
to the target task and can obtain better solutions with less computational
cost. DECN is also able to effectively utilize the low-fidelity information of
the target task to form an efficient optimization strategy. The experiments on
nine synthetics and two real-world cases show the advantages of learned
optimization strategies over the state-of-the-art human-designed and
meta-learning EA baselines. In addition, due to the tensorization of the
operations, DECN is friendly to the acceleration provided by GPUs and runs 102
times faster than EA.
