A Review on Evolutionary Multitask Optimization: Trends and Challenges

Evolutionary algorithms (EAs) possess strong problem-solving abilities and have been applied in a wide range of applications. However, they still suffer from a high computational burden and poor generalization ability. To overcome the limitations, numerous studies consider conducting knowledge extraction across distinct optimization task domains. Among these research strands, one representative tributary is evolutionary multitask optimization (EMTO) that aims to resolve multiple optimization tasks simultaneously. The underlying attribute of implicit parallelism for EAs can well incorporate with the framework of EMTO, giving rise to the ascending EMTO studies. This review is intended to present a detailed exposition on the research in the EMTO area. We reveal the core components for designing the EMTO algorithms. Subsequently, we organize the works lying in the fusions between EMTO and traditional EAs. By analyzing the associations for diverse strategies in different branches of EMTO, this review uncovers the research trends and the potentially important directions, with additional interesting real-world applications mentioned.

Automated summary

This paper presents a detailed exposition on the research in the field of evolutionary multitask optimization (EMTO), revealing the core components of EMTO algorithms and the fusion between EMTO and traditional evolutionary algorithms. By analyzing the associations of different strategies in various branches of EMTO, this review uncovers research trends and potentially important directions, as well as mentions interesting real-world applications.