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The Scientific Landscape of Hyper-Heuristics: A Bibliometric Analysis Based on Scopus
AlgorithmsHyper-heuristics emerged as a broader metaheuristic framework to address the limitations of traditional optimization heuristics. By abstracting the design of low-level heuristics, hyper-heuristics offer a flexible and adaptable approach to solving ...
Helen C. Peñate-Rodríguez +3 more
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Hyper-heuristic decision tree induction [PDF]
2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), 2009 Hyper-heuristics are increasingly used in function and combinatorial optimization. Rather than attempt to solve a problem using a fixed heuristic, a hyper-heuristic approach attempts to find a combination of heuristics that solve a problem (and in turn may be directly suitable for a class of problem instances).
Alan Vella, David Corne, Chris Murphy
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A hyper-heuristic for adaptive scheduling in computational grids [PDF]
, 2007 In this paper we present the design and implementation of an hyper-heuristic for efficiently scheduling independent jobs in computational grids.
Xhafa Xhafa, Fatos
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A memetic algorithm for the university course timetabling problem [PDF]
, 2008 This article is posted here with permission from IEEE - Copyright @ 2008 IEEEThe design of course timetables for academic institutions is a very hectic job due to the exponential number of possible feasible timetables with respect to the problem size ...
Jat, SN, Yang, S
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Hyper-heuristics: A survey and taxonomy
Computers & 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.
Dokeroglu, Tansel +2 more
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Enhancing Selection Hyper-Heuristics via Feature Transformations [PDF]
IEEE Computational Intelligence Magazine, 2018 Hyper-heuristics are a novel tool. They deal with complex optimization problems where standalone solvers exhibit varied performance. Among such a tool reside selection hyper-heuristics. By combining the strengths of each solver, this kind of hyper-heuristic offers a more robust tool.
Amaya, I. +6 more
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Advanced Intelligent Discovery, EarlyView.This work introduces a novel framework for identifying non‐small cell lung cancer biomarkers from hundreds of volatile organic compounds in breath, analyzed via gas chromatography‐mass spectrometry. This method integrates generative data augmentation and multi‐view feature selection, providing a stable and accurate solution for biomarker discovery in ...
Guancheng Ren +10 more
wiley +1 more source
Advanced Intelligent Systems, EarlyView.This study provides an introduction to Bayesian optimisation targeted for experimentalists. It explains core concepts, surrogate modelling, and acquisition strategies, and addresses common real‐world challenges such as noise, constraints, mixed variables, scalability, and automation.
Chuan He +2 more
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Application of Heuristic Combinations within a Hyper-Heuristic Framework for Exam Timetabling
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 2020 Examination Timetabling Problem is one of the optimization and combinatorial problems. It is proved to be a non-deterministic polynomial (NP)-hard problem.
Gabriella Icasia +2 more
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A Sequence-Based Hyper-Heuristic for Traveling Thieves
Applied Sciences, 2022 A plethora of combinatorial optimization problems can be linked to real-life decision scenarios. Even nowadays, more diverse and complex problems are popping up. One of these problems is the traveling thief problem (TTP), which combines elements from the
Daniel Rodríguez +3 more
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