Optimization algorithms are very different from human optimizers. A human being would gain more experiences through problem-solving, which helps her/him in solving a new unseen problem. Yet an optimization algorithm never gains any experiences by solving more problems. In recent years, efforts have been made towards endowing optimization algorithms with some abilities of experience learning, which is regarded as experience-based optimization. In this paper, we argue that hard optimization problems could be tackled efficiently by making better use of experiences gained in related problems. We demonstrate our ideas in the context of expensive optimization, where we aim to find a near-optimal solution to an expensive optimization problem with as few fitness evaluations as possible. To achieve this, we propose an experience-based surrogate-assisted evolutionary algorithm (SAEA) framework to enhance the optimization efficiency of expensive problems, where experiences are gained across related expensive tasks via a novel meta-learning method. These experiences serve as the task-independent parameters of a deep kernel learning surrogate, then the solutions sampled from the target task are used to adapt task-specific parameters for the surrogate. With the help of experience learning, competitive regression-based surrogates can be initialized using only 1$d$ solutions from the target task ($d$ is the dimension of the decision space). Our experimental results on expensive multi-objective and constrained optimization problems demonstrate that experiences gained from related tasks are beneficial for the saving of evaluation budgets on the target problem.

翻译：优化算法与人类优化者截然不同。人类通过解决问题能积累更多经验，从而有助于处理未见过的新问题。然而，优化算法在解决更多问题时却从未获得任何经验。近年来，研究者致力于赋予优化算法一定的经验学习能力，这被称为基于经验的优化。本文认为，通过更好地利用相关问题中获得的经验，可以有效解决困难优化问题。我们在昂贵优化的背景下阐述这一思想，目标是尽可能少地进行适应度评估，从而找到昂贵优化问题的近似最优解。为此，我们提出了一种基于经验的代理辅助进化算法（SAEA）框架，通过一种新颖的元学习方法跨相关昂贵任务获取经验，以提升昂贵问题的优化效率。这些经验作为深度核学习代理的任务独立参数，随后利用目标任务采样的解来适配代理的任务特定参数。借助经验学习，仅需目标任务中1d个解（d为决策空间维度）即可初始化具有竞争力的回归代理。在昂贵多目标及约束优化问题上的实验结果表明，从相关任务中获取的经验有助于节省目标问题的评估预算。