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
doaj +5 more sources
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 +4 more
doaj +3 more sources
A multi-objective hyper-heuristic based on choice function [PDF]
Expert Systems With Applications, 2014 Hyper-heuristics are emerging methodologies that perform a search over the space of heuristics in an attempt to solve difficult computational optimization problems.
Mashael S Maashi +2 more
exaly +4 more sources
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
doaj +2 more sources
A Dynamic Multiarmed Bandit-Gene Expression Programming Hyper-Heuristic for Combinatorial Optimization Problems [PDF]
IEEE Transactions on Cybernetics, 2015 Hyper-heuristics are search methodologies that aim to provide high-quality solutions across a wide variety of problem domains, rather than developing tailor-made methodologies for each problem instance/domain.
Nasser R Sabar, Graham Kendall, Rong Qu
exaly +6 more sources
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
doaj +3 more sources
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
doaj +2 more sources
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
doaj +2 more sources
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
doaj +2 more sources
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
core +4 more sources