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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.
Jaime Simão SICHMAN +2 more
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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
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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.
Hector G Ceballos +2 more
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Choice Function-Based Hyper-Heuristics for Causal Discovery under Linear Structural Equation Models [PDF]
BiomimeticsCausal discovery is central to human cognition, and learning directed acyclic graphs (DAGs) is its foundation. Recently, many nature-inspired meta-heuristic optimization algorithms have been proposed to serve as the basis for DAG learning.
Yinglong Dang +2 more
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A Feature-Independent Hyper-Heuristic Approach for Solving the Knapsack Problem
Applied Sciences, 2021 Recent years have witnessed a growing interest in automatic learning mechanisms and applications. The concept of hyper-heuristics, algorithms that either select among existing algorithms or generate new ones, holds high relevance in this matter.
Xavier Sánchez-Díaz +5 more
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Algorithms, 2022 Hyper-heuristics are widely used for solving numerous complex computational search problems because of their intrinsic capability to generalize across problem domains.
Stephen A. Adubi +2 more
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Identifying Hyper-Heuristic Trends through a Text Mining Approach on the Current Literature
Applied Sciences, 2022 Hyper-heuristics have arisen as methods that increase the generality of existing solvers. They have proven helpful for dealing with complex problems, particularly those related to combinatorial optimization.
Anna Karen Gárate-Escamilla +4 more
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PHH: Policy-Based Hyper-Heuristic With Reinforcement Learning
IEEE Access, 2023 Hyper-heuristics have a high level of generality and adaptability, allowing them to effectively solve a wide range of complex optimization problems.
Orachun Udomkasemsub +2 more
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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
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Evolution of group-theoretic cryptology attacks using hyper-heuristics
Journal of Mathematical Cryptology, 2021 In previous work, we developed a single evolutionary algorithm (EA) to solve random instances of the Anshel–Anshel–Goldfeld (AAG) key exchange protocol over polycyclic groups. The EA consisted of six simple heuristics which manipulated strings.
Craven Matthew J., Woodward John R.
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