Results 11 to 20 of about 1,116,663 (238)
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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AlgorithmsThis study introduces a novel train-and-test approach referred to as apprenticeship learning (AL) for generating selection hyper-heuristics to solve the Quadratic Unconstrained Binary Optimisation (QUBO) problem.
Jack Cakebread +4 more
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Hyper-heuristics for grouping problems. [PDF]
, 2015 Grouping problems are hard to solve combinatorial optimization problems which require partitioning of objects into a minimum number of subsets while another additional objective is simultaneously optimized. Considerable research e ort has recently been directed towards automated problem-independent reusable heuristic search methodologies such as hyper ...
Elhag, Anas
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A Novel Hyper-Heuristic Algorithm with Soft and Hard Constraints for Causal Discovery Using a Linear Structural Equation Model [PDF]
EntropyArtificial intelligence plays an indispensable role in improving productivity and promoting social development, and causal discovery is one of the extremely important research directions in this field. Acyclic directed graphs (DAGs) are the most commonly
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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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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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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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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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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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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