Towards the Design of Heuristics by Means of Self-Assembly [PDF]
Electronic Proceedings in Theoretical Computer Science, 2010 The current investigations on hyper-heuristics design have sprung up in two different flavours: heuristics that choose heuristics and heuristics that generate heuristics.
Natalio Krasnogor +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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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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Ensemble move acceptance in selection hyper-heuristics [PDF]
, 2016 Selection hyper-heuristics are high level search methodologies which control a set of low level heuristics while solving a given problem. Move acceptance is a crucial component of selection hyper-heuristics, deciding whether to accept or reject a new ...
Kheiri, Ahmed +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 steady state micro genetic algorithm for hyper-heuristic generation in one-dimensional bin packing [PDF]
Scientific ReportsThe one-dimensional bin packing problem (1DBPP) is a well-known NP-hard problem in computer science and operations research that involves many real-world applications.
Julio Juárez +2 more
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Nested Markov chain hyper-heuristic (NMHH): a hybrid hyper-heuristic framework for single-objective continuous problems. [PDF]
PeerJ Comput SciThis 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 sequences and simulated annealing on the hyperlevel, which evolves the chain and the operator parameters.
Bándi N, Gaskó N.
europepmc +4 more sources
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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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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