A Unified Framework of Graph-based Evolutionary Multitasking Hyper-heuristic

In recent research, hyper-heuristics have attracted increasing attention among researchers in various fields. The most appealing feature of hyper-heuristics is that they aim to provide more generalized solutions to optimization problems by searching in a high-level space of heuristics instead of direct problem domains. Despite the good generalities of hyperheuristics, the design of more general search methodologies is still an emerging challenge. Evolutionary multitasking is a relatively new evolutionary paradigm that attempts to solve multiple optimization problems simultaneously. It exploits the underlying similarities among different optimization tasks by allowing the transmission of information among them, thus accelerating the optimization of all tasks. Inherently, hyper-heuristics and evolutionary multitasking share similarities in three ways. (1) They both operate on third-party search spaces. (2) High level search methodologies are universal. (3) They both conduct cross-domain optimization. To integrate their advantages, i.e., the knowledge-transfer and the cross-domain optimization abilities of the evolutionary multitasking and the search in the heuristic spaces of hyper-heuristics, in this paper, a unified framework of evolutionary multitasking graph-based hyper-heuristic (EMHH) is thereby proposed. To assess the generality and effectiveness of EMHH, the integration of the population-based graph-based hyper-heuristics with the evolutionary multitasking for solving exam timetabling and graph-coloring problems, separately and simultaneously, is studied. The experimental results demonstrate the effectiveness, efficiency, and increased generality of the proposed unified framework compared with single-tasking hyperheuristics