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.

中文翻译：

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基于图的进化多任务超启发式统一框架


在最近的研究中，超启发式在各个领域引起了越来越多的关注。超启发式最吸引人的特征是，它们旨在通过在启发式的高级空间而不是直接问题域中搜索来为优化问题提供更通用的解决方案。尽管超启发式研究取得了有希望的发现，但更通用的搜索方法的设计仍然是一项关键研究。进化多任务是一种相对较新的进化范式，试图同时解决多个优化问题。它通过在不同优化任务之间传递信息来利用不同优化任务之间的潜在相似性，从而加速所有任务的优化。从本质上讲，超启发式和进化多任务在以下三个方面是相似的：1）它们都在第三方搜索空间上运行； 2）高级搜索方法是通用的； 3）它们都进行跨域优化。为了有效地整合它们的优势，即进化多任务的知识转移和跨域优化以及超启发式的启发空间搜索，本文提出了一种基于图的进化多任务超启发式的统一框架（EMHH） ）提出。为了评估 EMHH 的通用性和有效性，研究了基于群体的基于图的超启发式与进化多任务的集成，以分别和同时解决考试时间表和图着色问题。实验结果证明了与单任务超启发式方法相比，所提出的统一框架的有效性、效率和通用性。