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Evolutionary algorithms (EAs) have emerged as a powerful framework foroptimization, especially for black-box optimization. This paper first focuseson automated EA: Automated EA exploits structure in the problem of interest toautomatically generate update rules (optimization strategies) for generatingand selecting potential solutions so that it can move a random population nearthe optimal solution. However, current EAs cannot achieve this goal due to thepoor representation of the optimization strategy and the weak interactionbetween the optimization strategy and the target task. We design a deepevolutionary convolution network (DECN) to realize the move from hand-designedEAs to automated EAs without manual interventions. DECN has high adaptabilityto the target task and can obtain better solutions with less computationalcost. DECN is also able to effectively utilize the low-fidelity information ofthe target task to form an efficient optimization strategy. The experiments onnine synthetics and two real-world cases show the advantages of learnedoptimization strategies over the state-of-the-art human-designed andmeta-learning EA baselines. In addition, due to the tensorization of theoperations, DECN is friendly to the acceleration provided by GPUs and runs 102times faster than EA.

DECN: Automated Evolutionary Algorithms via Evolution Inspired Deep Convolution Network