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.

翻译：摘要：进化算法已成为一种强大的优化框架，尤其适用于黑箱优化。本文首先聚焦于自动化进化算法：自动化进化算法通过利用待解决问题中的结构，自动生成用于生成和选择潜在解的更新规则（优化策略），从而将随机种群移近最优解。然而，由于优化策略表示能力不足及优化策略与目标任务之间的弱交互，当前进化算法无法实现这一目标。我们设计了一种深度进化卷积网络（DECN），以实现从人工设计的进化算法到无需人工干预的自动化进化算法的转变。DECN对目标任务具有高度适应性，能够以较低的计算成本获得更优解。此外，DECN还能有效利用目标任务的低保真度信息，形成高效的优化策略。在九个合成案例和两个实际案例上的实验表明，学习得到的优化策略优于最先进的人工设计和元学习进化算法基线。另外，由于操作的张量化，DECN对GPU加速友好，其运行速度比传统进化算法快102倍。