Evolutionary Multitasking via Reinforcement Learning

Different from traditional evolutionary algorithms (EAs), the multifactorial evolutionary algorithm (MFEA) is proposed to optimize multiple optimization tasks concurrently. Through the knowledge transfer between different tasks, MFEA has been proved to be superior to single-task EAs in the solution quality and convergence speed. Recently, various MFEAs have been developed. Most of them are based on a common model in MFEA, where a fixed knowledge transfer parameter, i. e. , random mating probability (rmp), is used. In addition, a single evolutionary search operator is employed in the whole evolutionary process. However, in this model, the fixed rmp is difficult to adapt to multiple different tasks. Besides, a single evolutionary search operator may not be suitable for problems with different properties, thus limiting the performance of the algorithm. Based on these considerations, in this article, a reinforcement learning based multifactorial evolutionary algorithm (RLMFEA) is presented. In RLMFEA, it allows different evolutionary search operators to be embedded in MFEA, and each task has a changing rmp that is adaptively adjusted by reinforcement learning. The effectiveness of RLMFEA has been verified on a series of single-objective multitask optimization benchmark functions and a real-world application.