Evolutionary algorithms (EAs) have emerged as a powerful framework for expensive black-box optimization. Obtaining better solutions with less computational cost is essential and challenging for black-box optimization. The most critical obstacle is figuring out how to effectively use the target task information to form an efficient optimization strategy. However, current methods are weak due to the poor representation of the optimization strategy and the inefficient interaction between the optimization strategy and the target task. To overcome the above limitations, we design a learned EA (LEA) to realize the move from hand-designed optimization strategies to learned optimization strategies, including not only hyperparameters but also update rules. Unlike traditional EAs, LEA has high adaptability to the target task and can obtain better solutions with less computational cost. LEA is also able to effectively utilize the low-fidelity information of the target task to form an efficient optimization strategy. The experimental results on one synthetic case, CEC 2013, and two real-world cases show the advantages of learned optimization strategies over human-designed baselines. In addition, LEA is friendly to the acceleration provided by Graphics Processing Units and runs 102 times faster than unaccelerated EA when evolving 32 populations, each containing 6400 individuals.

翻译：进化算法已成为昂贵黑箱优化领域的强大框架。以更少的计算代价获得更优解是黑箱优化的核心挑战，其关键障碍在于如何有效利用目标任务信息形成高效优化策略。然而，当前方法因优化策略表征能力不足及策略与目标任务交互效率低下而表现薄弱。为克服上述局限，我们设计了学习型进化算法（LEA），实现从手工设计的优化策略向学习型优化策略的转变——不仅涵盖超参数，更包含更新规则。与传统进化算法不同，LEA对目标任务具有高度适应性，能以更少计算代价获得更优解。LEA还能有效利用目标任务的低保真度信息形成高效优化策略。在合成案例、CEC 2013基准测试及两个实际案例中的实验结果表明，学习型优化策略优于人类设计的基线方法。此外，LEA对图形处理器加速友好，当演化32个种群（每种群包含6400个个体）时，其运行速度比未加速的进化算法快102倍。