Results 11 to 20 of about 1,200,755 (251)
Assessing hyper-heuristic performance [PDF]
Journal of the Operational Research Society, 2020 Limited attention has been paid to assessing the generality performance of hyper-heuristics. The performance of hyper-heuristics has been predominately assessed in terms of optimality which is not ideal as the aim of hyper-heuristics is not to be competitive with state of the art approaches but rather to raise the level of generality, i.e.
Nelishia Pillay, Rong Qu
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Nested Markov chain hyper-heuristic (NMHH): a hybrid hyper-heuristic framework for single-objective continuous problems [PDF]
PeerJ Computer ScienceThis article introduces a new hybrid hyper-heuristic framework that deals with single-objective continuous optimization problems. This approach employs a nested Markov chain on the base level in the search for the best-performing operators and their ...
Nándor Bándi, Noémi Gaskó
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A Novel Cooperative Multi-Stage Hyper-Heuristic for Combination Optimization Problems
Complex System Modeling and Simulation, 2021 A hyper-heuristic algorithm is a general solution framework that adaptively selects the optimizer to address complex problems. A classical hyper-heuristic framework consists of two levels, including the high-level heuristic and a set of low-level ...
Fuqing Zhao, Jie Cao, Jianxin Tang
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Searching the Hyper-heuristic Design Space [PDF]
Cognitive Computation, 2013 We extend a previous mathematical formulation of hyper-heuristics to reflect the emerging generalization of the concept. We show that this leads naturally to a recursive definition of hyper-heuristics and to a division of responsibility that is suggestive of a blackboard architecture, in which individual heuristics annotate a shared workspace with ...
Jerry Swan +4 more
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A Self-Learning Hyper-Heuristic Algorithm Based on a Genetic Algorithm: A Case Study on Prefabricated Modular Cabin Unit Logistics Scheduling in a Cruise Ship Manufacturer [PDF]
BiomimeticsHyper-heuristic algorithms are known for their flexibility and efficiency, making them suitable for solving engineering optimization problems with complex constraints.
Jinghua Li +4 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 W. Corne, Chris Murphy
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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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HYPER HEURISTIC EVOLUTIONARY APPROACH FOR CONSTRUCTING DECISION TREE CLASSIFIERS
Journal of ICT, 2021 Decision tree models have earned a special status in predictive modeling since these are considered comprehensible for human analysis and insight.
Saroj Ratnoo +2 more
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Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation, 2015 Practitioners often need to solve real world problems for which no custom search algorithms exist. In these cases they tend to use general-purpose solvers that have no guarantee to perform well on their specific problem. The relatively new field of hyper-heuristics provides an alternative to the potential pit-falls of general-purpose solvers, by ...
John R. Woodward, Daniel R. Tauritz
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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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