A Classification of Hyper-heuristic Approaches

By Edmund Kieran Burke, Matthew R. Hyde, Graham Kendall, Gabriela Ochoa, Ender Özcan, John Robert Woodward. 2010

583 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/dde767d74767059e5f4d435af59c349c20cca413

How researchers see this paper

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

In Artificial Intelligence

In Computational Theory and Mathematics

In Management Science and Operations Research

Citations in the corpus

Automated Design of Production Scheduling Heuristics: A Review (2016, methodology): “[2] classified hyper-heuristics with respect to the nature of their process, i.”
On Using Surrogates with Genetic Programming (2015, methodology): “Most of these approaches use Genetic Programming (Koza, 1992; Poli et al., 2008) and can be classiﬁed as applications of hyper-heuristic search (Burke et al., 2009, 2010).”
A unified solution framework for multi-attribute vehicle routing problems (2014, methodology): “Several well-known VRP heuristics are included in this category and are displayed in Table 1: the Unified Tabu Search (UTS; Cordeau et al. 1997, 2001, Cordeau and Laporte 2001, 2003, Cordeau et al. 2004), the Adaptive Large Neighborhood Search algorithm (ALNS; Ropke and Pisinger 2006a,b, Pisinger an”
Heuristics for multi-attribute vehicle routing problems: A survey and synthesis (2013, background): “Evolving search parameters directly within the genetic material of individuals is a common practice in EAs, while general metaheuristics adaptation is a main focus of hyper-heuristics (Burke et al. 2010).”
Bio-inspired computation: Where we stand and what's next (2019, methodology): “Hyper-heuristics operate on a set of simple heuristics – which by themselves do not render good results for a given optimization problem – and combine them via a higher-level algorithm towards achieving much better solutions [126].”
Constraint-handling in nature-inspired numerical optimization: Past, present and future (2011, background): “Hyper-heuristics, are approaches in which several metaheuristics act at different stages during the search, as determined by a centralized control mechanism [217].”