A Tabu-Search Hyperheuristic for Timetabling and Rostering

By Edmund Kieran Burke, Graham Kendall, Eric Soubeiga. J. Heuristics, 2003

596 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Industrial and Manufacturing Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/0e784a65ea4df380af9003a0bebda3a19aca02fa

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In Computer Networks and Communications

In Industrial and Manufacturing Engineering

In Management Science and Operations Research

Citations in the corpus

A Classification of Hyper-heuristic Approaches (2010, methodology): “A simulated annealing hyper-heuristic for determining shipper sizes: In [23] the tabu search hyper-heuristic, originally presented in [12], is integrated within a simulated annealing framework.”
A survey on new generation metaheuristic algorithms (2019, background): “Cheng et al. (2016) propose Brain Storm Optimization (BSO) algorithm. BSO mimics the process of human brainstorming. Individuals are grouped and diverged in the search space. Gonçalves et al. (2015) present Search Group Algorithm (SGA), to optimize truss structures. The efficiency of SGA is compared”
Memetic algorithms and memetic computing optimization: A literature review (2012, methodology): “A further example of choice function approach is given in [20] where a tabu list is employed to classify the success of the memes and their activation schedule.”
A survey of metaheuristic-based techniques for University Timetabling problems (2007, methodology): “However, we appreciate that other techniques, such as Neural Networks and Hyper-heuristics, might also be considered metaheuristics by some, and it is worth mentioning that there have also been successful applications of these latter techniques to various timetabling problems in the past ( Burke et ”
A graph-based hyper-heuristic for educational timetabling problems (2007, methodology): “We test our approach with the same number of runs as that of the “Hyper-heuristic” from (Burke, Kendall and Soubeiga, 2003) to make a more fair comparison.”
Reinforcement Learning: A Tutorial Survey and Recent Advances (2009, unknown): “The work related to hyperheuristics (Burke et al., 2003a,b; Nareyek, 2003) can be used when RL is to be used dynamically to select a meta-heuristic.”
Classification of adaptive memetic algorithms: a comparative study (2006, background): “For example, the constraints may take the form of , for , where is the number of constraints.”
A generic framework for constrained optimization using genetic algorithms (2005, background): “The interested reader is referred to [2] and [3] for more reading.”