Final published version
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter
Hyper-heuristics. / Epitropakis, M.G.; Burke, E.K.
Handbook of Heuristics. Vol. 1-2 Springer International Publishing, 2018. p. 489-545.Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter
}
TY - CHAP
T1 - Hyper-heuristics
AU - Epitropakis, M.G.
AU - Burke, E.K.
PY - 2018
Y1 - 2018
N2 - This chapter presents a literature review of the main advances in the field of hyper-heuristics, since the publication of a survey paper in 2013. The chapter demonstrates the most recent advances in hyper-heuristic foundations, methodologies, theory, and application areas. In addition, a simple illustrative selection hyper-heuristic framework is developed as a case study. This is based on the well-known Iterated Local Search algorithm and is presented to provide a tutorial style introduction to some of the key basic issues. A brief discussion about the implementation process in addition to the decisions that had to be made during the implementation is presented. The framework implements an action selection model that operates on the perturbation stage of the Iterated Local Search algorithm to adaptively select among various low-level perturbation heuristics. The performance and efficiency of the developed framework is evaluated across six well-known real-world problem domains. © Springer International Publishing AG, part of Springer Nature 2018. All rights reserved.
AB - This chapter presents a literature review of the main advances in the field of hyper-heuristics, since the publication of a survey paper in 2013. The chapter demonstrates the most recent advances in hyper-heuristic foundations, methodologies, theory, and application areas. In addition, a simple illustrative selection hyper-heuristic framework is developed as a case study. This is based on the well-known Iterated Local Search algorithm and is presented to provide a tutorial style introduction to some of the key basic issues. A brief discussion about the implementation process in addition to the decisions that had to be made during the implementation is presented. The framework implements an action selection model that operates on the perturbation stage of the Iterated Local Search algorithm to adaptively select among various low-level perturbation heuristics. The performance and efficiency of the developed framework is evaluated across six well-known real-world problem domains. © Springer International Publishing AG, part of Springer Nature 2018. All rights reserved.
KW - Black box optimization
KW - Combinatorial optimization
KW - Dynamic optimization
KW - Evolutionary computation
KW - Heuristics
KW - Hyper-heuristics
KW - Iterated local search
KW - Machine learning
KW - Meta-heuristics
KW - Multi-objective optimization
KW - Optimization
KW - Packing
KW - Scheduling
KW - Search
KW - Timetabling
U2 - 10.1007/978-3-319-07124-4_32
DO - 10.1007/978-3-319-07124-4_32
M3 - Chapter
SN - 9783319071237
VL - 1-2
SP - 489
EP - 545
BT - Handbook of Heuristics
PB - Springer International Publishing
ER -