The cognitive ability to learn with experience is a hallmark of intelligent systems. The emerging transfer optimization paradigm pursues such human-like problem-solving prowess by leveraging useful information from various source tasks to enhance optimization efficiency on a related target task. The occurrence of harmful negative transfer is a key concern in this setting, paving the way for recent probabilistic model-based transfer evolutionary algorithms that curb this phenomenon. However, in applications where the source and target domains, i.e., the features of their respective search spaces (e.g., dimensionality) and the distribution of good solutions in those spaces, do not match, narrow focus on curbing negative effects can lead to the conservative cancellation of knowledge transfer. Taking this cue, this paper presents a novel perspective on domain adaptation in the context of evolutionary optimization, inducing positive transfers even in scenarios of source-target domain mismatch. Our first contribution is to establish a probabilistic formulation of domain adaptation, by which source and/or target tasks can be mapped to a common solution representation space in which their discrepancy is reduced. Secondly, a domain adaptive transfer evolutionary algorithm is proposed, supporting both offline construction and online data-driven learning of non-linear mapping functions. The performance of the algorithm is experimentally verified, demonstrating superior convergence rate in comparison to state-of-the-art baselines on synthetic benchmarks and a practical case study in multi-location inventory planning. Our results thus shed light on a new research direction for optimization algorithms that improve their efficacy by learning from heterogeneous experiential priors.

通过经验学习的认知能力是智能系统的标志。通过利用来自各种源任务的有用信息来提高相关目标任务的优化效率，新兴的传输优化范例追求着类似人类的问题解决能力。在这种情况下，有害负转移的发生是关键问题，这为遏制这种现象的最新基于概率模型的转移进化算法铺平了道路。但是，在源域和目标域即的应用程序中。，它们各自的搜索空间的特征（例如维数）和这些空间中好的解决方案的分布是不匹配的，仅关注于抑制负面影响会导致知识转移的保守取消。以此为线索，本文提出了在进化优化背景下域适应的新观点，即使在源-目标域不匹配的情况下，也能引起正向转移。我们的第一个贡献是建立域适应的概率表述，通过它可以将源任务和/或目标任务映射到一个通用的解决方案表示空间，从而减少了它们之间的差异。其次，提出了一种领域自适应转移进化算法，既支持离线构建又支持在线数据驱动的学习。非线性映射函数。该算法的性能已通过实验验证，与综合基准上的最新基准相比，具有更高的收敛速度，并且在多地点库存计划中具有实际案例研究。因此，我们的结果为优化算法提供了新的研究方向，该算法通过从异构经验先验中学习来提高其效率。