  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.
