Multi-Tasking Evolutionary Algorithm (MTEA) for Single-Objective Continuous Optimization

Multi-task learning uses auxiliary data or knowledge from relevant tasks to facilitate the learning in a new task. Multi-task optimization applies multi-task learning to optimization to study how to effectively and efficiently tackle multiple optimization problems simultaneously. Evolutionary multi-tasking, or multi-factorial optimization, is an emerging subfield of multi-task optimization, which integrates evolutionary computation and multi-task learning. This paper proposes a novel easy-to-implement multi-tasking evolutionary algorithm (MTEA), which copes well with significantly different optimization tasks by estimating and using the bias among them. Comparative studies with eight state-of-the-art single- and multi-task approaches in the literature on nine benchmarks demonstrated that on average the MTEA outperformed all of them, and has lower computational cost than six of them. Particularly, unlike other multi-task algorithms, the performance of the MTEA is consistently good whether the tasks are similar or significantly different, making it ideal for real-world applications.