A Unified Framework of Graph-Based Evolutionary Multitasking Hyper-Heuristic

In recent research, hyper-heuristics have attracted increasing attention 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 promising findings in hyper-heuristics, the design of more general search methodologies still presents a key research. Evolutionary multitasking is a relatively new evolutionary paradigm which attempts to solve multiple optimization problems simultaneously. It exploits the underlying similarities among different optimization tasks by transferring information among them, thus accelerating the optimization of all tasks. Inherently, hyper-heuristics and evolutionary multitasking are similar in the following three ways: 1) they both operate on third-party search spaces; 2) high-level search methodologies are universal; and 3) they both conduct cross-domain optimization. To integrate their advantages effectively, i.e., the knowledge-transfer and cross-domain optimization of evolutionary multitasking and the search in the heuristic spaces of hyper-heuristics, in this article, a unified framework of evolutionary multitasking graph-based hyper-heuristic (EMHH) is proposed. To assess the generality and effectiveness of the EMHH, population-based graph-based hyper-heuristics integrated with evolutionary multitasking to solve exam timetabling and graph-coloring problems, separately and simultaneously, are studied. The experimental results demonstrate the effectiveness, efficiency, and increased the generality of the proposed unified framework compared with single-tasking hyper-heuristics.

Automated summary

Hyper-heuristics and evolutionary multitasking share similarities in search methods, and by combining the advantages of both, the optimization of problems can be accelerated, leading to increased generality.