Hyper-heuristics

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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

Harvard

Epitropakis, MG & Burke, EK 2018, Hyper-heuristics. in Handbook of Heuristics. vol. 1-2, Springer International Publishing, pp. 489-545. https://doi.org/10.1007/978-3-319-07124-4_32

APA

Epitropakis, M. G., & Burke, E. K. (2018). Hyper-heuristics. In Handbook of Heuristics (Vol. 1-2, pp. 489-545). Springer International Publishing. https://doi.org/10.1007/978-3-319-07124-4_32

Vancouver

Epitropakis MG , Burke EK. Hyper-heuristics. In Handbook of Heuristics. Vol. 1-2. Springer International Publishing. 2018. p. 489-545 doi: 10.1007/978-3-319-07124-4_32

Author

Epitropakis, M.G. ; Burke, E.K. / Hyper-heuristics. Handbook of Heuristics. Vol. 1-2 Springer International Publishing, 2018. pp. 489-545

Bibtex

@inbook{4c0ac4c795994fd8983eddf829a15bc8,

title = "Hyper-heuristics",

abstract = "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. {\textcopyright} Springer International Publishing AG, part of Springer Nature 2018. All rights reserved.",

keywords = "Black box optimization, Combinatorial optimization, Dynamic optimization, Evolutionary computation, Heuristics, Hyper-heuristics, Iterated local search, Machine learning, Meta-heuristics, Multi-objective optimization, Optimization, Packing, Scheduling, Search, Timetabling",

author = "M.G. Epitropakis and E.K. Burke",

year = "2018",

doi = "10.1007/978-3-319-07124-4_32",

language = "English",

isbn = "9783319071237",

volume = "1-2",

pages = "489--545",

booktitle = "Handbook of Heuristics",

publisher = "Springer International Publishing",

}

RIS

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 -

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