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Recent advances in selection hyper-heuristics
European Journal of Operational Research, 2020 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ahmed Kheiri +2 more
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A generality analysis of multiobjective hyper-heuristics
Information Sciences, 2023 Selection hyper-heuristics have emerged as high level general-purpose search methodologies that mix and control a set of low-level (meta) heuristics. Previous empirical studies over a range of single objective optimisation problems have shown that the ...
Wenwen Li +2 more
exaly +6 more sources
A review of reinforcement learning based hyper-heuristics [PDF]
PeerJ Computer ScienceThe reinforcement learning based hyper-heuristics (RL-HH) is a popular trend in the field of optimization. RL-HH combines the global search ability of hyper-heuristics (HH) with the learning ability of reinforcement learning (RL). This synergy allows the
Cuixia Li +4 more
doaj +3 more sources
Applied Sciences (Switzerland), 2021 As exact algorithms are unfeasible to solve real optimization problems, due to their computational complexity, meta-heuristics are usually used to solve them.
Vinicius Renan DE CARVALHO +2 more
exaly +3 more sources
Hyper-heuristics for personnel scheduling domains
Artificial Intelligence, 2022 In real-life applications problems can frequently change or require small adaptations. Manually creating and tuning algorithms for different problem domains or different versions of a problem can be cumbersome and time-consuming. In this paper we consider several important problems with high practical relevance, which are Bus Driver Scheduling ...
Lucas Kletzander, Nysret Musliu
core +4 more sources
Hyper-heuristics: a survey of the state of the art [PDF]
Journal of the Operational Research Society, 2013 Hyper-heuristics comprise a set of approaches that are motivated (at least in part) by the goal of automating the design of heuristic methods to solve hard computational search problems. An underlying strategic research challenge is to develop more generally applicable search methodologies.
Edmund K. Burke +6 more
openaire +6 more sources
A Systematic Review of Hyper-Heuristics on Combinatorial Optimization Problems
IEEE Access, 2020 Hyper-heuristics aim at interchanging different solvers while solving a problem. The idea is to determine the best approach for solving a problem at its current state. This way, every time we make a move it gets us closer to a solution.
Melissa Sanchez +2 more
exaly +3 more sources
Hyper-heuristics: A survey and taxonomy
Computers and Industrial EngineeringHyper-heuristics are search techniques for selecting, generating, and sequencing (meta)-heuristics to solve challenging optimization problems. They differ from traditional (meta)-heuristics methods, which primarily employ search space-based optimization strategies.
Tansel Dokeroglu +2 more
exaly +3 more sources
Selection hyper-heuristics in dynamic environments [PDF]
Journal of the Operational Research Society, 2013 Current state-of-the-art methodologies are mostly developed for stationary optimization problems. However, many real-world problems are dynamic in nature, where different types of changes may occur over time. Population-based approaches, such as evolutionary algorithms, are frequently used for solving dynamic environment problems.
Berna Kiraz +2 more
openaire +3 more sources
Beyond Hyper-Heuristics: A Squared Hyper-Heuristic Model for Solving Job Shop Scheduling Problems
IEEE Access, 2022 Hyper-heuristics (HHs) stand as a relatively recent approach to solving optimization problems. There are different kinds of HHs. One of them deals with how low-level heuristics must be combined to deliver an improved solution to a set of problem ...
Alonso Vela +3 more
doaj +2 more sources