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 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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An Investigation of Hybrid Framework for Dynamic Multi Objective Problems
Gazi Üniversitesi Fen Bilimleri Dergisi, 2018 Multi-objective evolutionary algorithms and selection hyper-heuristics are adaptive methods that can handle different types of dynamism which may occur in the environment.
Berna KİRAZ
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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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MatHH: A Matlab-based Hyper-Heuristic framework
SoftwareX, 2022 Hyper-Heuristics (HHs) have proven to be a valuable tool for solving complex problems, such as Combinatorial Optimization Problems (COPs). These solvers have an assorted set of models arising through extensive research from the scientific community ...
Jorge M. Cruz-Duarte +2 more
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Hyper-Heuristic Approach for Improving Marker Efficiency [PDF]
AUTEX Research Journal, 2018 Marker planning is an optimization arrangement problem, where a set of cutting parts need to be placed on a thin paper without overlapping to create a marker – an exact diagram of cutting parts that will be cut from a single spread.
Domović Daniel +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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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 ...
Daniel R. Tauritz, John Woodward
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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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