A Hyperheuristic Approach to Scheduling a Sales Summit

By Peter I. Cowling, Graham Kendall, Eric Soubeiga. International Conference on the Practice and Theory of Automated Timetabling, 2000

662 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/9a2eae07b59389abaff5cee238da3fff4bf86ef1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

In Industrial and Manufacturing Engineering

In Management Science and Operations Research

Citations in the corpus

A tutorial for competent memetic algorithms: model, taxonomy, and design issues (2005, background): “Furthermore, the more recent approach to optimization called “hyperheuristics,” in particular, those described in [80] and [81] can be considered to be multimeme algorithms, where the set of low-level operators (i.”
Meta-Lamarckian learning in memetic algorithms (2004, neutral): “[20], where several heuristic methods are employed.”
A MAX-MIN Ant System for the University Course Timetabling Problem (2002, methodology): “These have been used within evolutionary methods and other generic search methods, either as ‘hyper-heuristics’ [1, 7], or to repair or decode indirect solution representations [16].”
Recent advances in selection hyper-heuristics (2020, background): “Note that traditional hyper-heuristics based on the framework initially proposed by Cowling et al. (2001) only require limited information to operate, such as the number of low-level heuristics, the direction of the optimisation process (maximising or minimising) and the objective function value of…”
Bio-inspired computation: Where we stand and what's next (2019, background): “• The second generation of Memetic Algorithms delved into the memetic transmission and design selection, with Multi-meme evolutionary algorithms [266] and hyper-heuristics [267] leading the algorithmic streamline during this period.”
Self-adaptive learning based particle swarm optimization (2011, background): “Successful examples were observed on combinatorial optimization problems [7,13,14], global numerical optimization problems [33,43,46,54,59], and multi-objective optimization problems [58].”
A survey on new generation metaheuristic algorithms (2019, background): “Cowling et al. (2000) analyze different hyperheuristics for a real-world personnel scheduling problem.”
A Classification of Hyper-heuristic Approaches (2010, methodology): “The heuristic selection can be done in a nonadaptive (simple) way: either randomly or along a cycle, based on a prefixed heuristic ordering [18, 19].”
Memetic algorithms and memetic computing optimization: A literature review (2012, background): “, [20, 37, 90, 95], where the coordination of the memes is performed by means of heuristic rules”