Evolutionary multitasking (EMT) is an emerging approach for solving multitask optimization problems (MTOPs) and has garnered considerable research interest. The implicit EMT is a significant research branch that utilizes evolution operators to enable knowledge transfer (KT) between tasks. However, current approaches in implicit EMT face challenges in adaptability, due to the use of a limited number of evolution operators and insufficient utilization of evolutionary states for performing KT. This results in suboptimal exploitation of implicit KT's potential to tackle a variety of MTOPs. To overcome these limitations, we propose a novel Learning to Transfer (L2T) framework to automatically discover efficient KT policies for the MTOPs at hand. Our framework conceptualizes the KT process as a learning agent's sequence of strategic decisions within the EMT process. We propose an action formulation for deciding when and how to transfer, a state representation with informative features of evolution states, a reward formulation concerning convergence and transfer efficiency gain, and the environment for the agent to interact with MTOPs. We employ an actor-critic network structure for the agent and learn it via proximal policy optimization. This learned agent can be integrated with various evolutionary algorithms, enhancing their ability to address a range of new MTOPs. Comprehensive empirical studies on both synthetic and real-world MTOPs, encompassing diverse inter-task relationships, function classes, and task distributions are conducted to validate the proposed L2T framework. The results show a marked improvement in the adaptability and performance of implicit EMT when solving a wide spectrum of unseen MTOPs.

翻译：进化多任务优化（EMT）是解决多任务优化问题（MTOPs）的一种新兴方法，已引起广泛的研究关注。隐式EMT是一个重要的研究分支，它利用进化算子实现任务间的知识迁移（KT）。然而，当前隐式EMT方法因使用的进化算子数量有限，且未能充分利用进化状态来执行KT，在适应性方面面临挑战。这导致隐式KT在应对各类MTOP时的潜力未能得到最优开发。为克服这些局限，我们提出了一种新颖的"学习迁移"（L2T）框架，以自动发现适用于当前MTOP的高效KT策略。该框架将KT过程概念化为学习智能体在EMT过程中的一系列策略决策。我们提出了用于决定迁移时机与方式的行为公式、包含进化状态信息特征的状态表示、关注收敛性与迁移效率增益的奖励公式，以及智能体与MTOP交互的环境。我们采用演员-批评家网络结构构建智能体，并通过近端策略优化算法进行学习。该学习得到的智能体可与多种进化算法集成，增强其解决各类新型MTOP的能力。我们在合成与真实世界的MTOP上进行了全面实证研究，涵盖多样化的任务间关系、函数类别和任务分布，以验证所提出的L2T框架。结果表明，在解决广泛未见过的MTOP时，隐式EMT的适应性和性能均有显著提升。