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A hyper-heuristic with a round robin neighbourhood selection. /
Kheiri, Ahmed; Özcan, Ender.
Evolutionary Computation in Combinatorial Optimization: 13th European Conference, EvoCOP 2013, Proceedings. Springer, 2013. p. 1-12 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7832 LNCS).
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Kheiri, A & Özcan, E 2013,
A hyper-heuristic with a round robin neighbourhood selection. in
Evolutionary Computation in Combinatorial Optimization: 13th European Conference, EvoCOP 2013, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7832 LNCS, Springer, pp. 1-12, 13th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2013, Vienna, Austria,
3/04/13.
https://doi.org/10.1007/978-3-642-37198-1_1
APA
Vancouver
Kheiri A, Özcan E.
A hyper-heuristic with a round robin neighbourhood selection. In Evolutionary Computation in Combinatorial Optimization: 13th European Conference, EvoCOP 2013, Proceedings. Springer. 2013. p. 1-12. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-37198-1_1
Author
Bibtex
@inproceedings{a355351e5f454f1998091c50d5e876e9,
title = "A hyper-heuristic with a round robin neighbourhood selection",
abstract = "An iterative selection hyper-heuristic passes a solution through a heuristic selection process to decide on a heuristic to apply from a fixed set of low level heuristics and then a move acceptance process to accept or reject the newly created solution at each step. In this study, we introduce Robinhood hyper-heuristic whose heuristic selection component allocates equal share from the overall execution time for each low level heuristic, while the move acceptance component enables partial restarts when the search process stagnates. The proposed hyper-heuristic is implemented as an extension to a public software used for benchmarking of hyper-heuristics, namely HyFlex. The empirical results indicate that Robinhood hyper-heuristic is a simple, yet powerful and general multistage algorithm performing better than most of the previously proposed selection hyper-heuristics across six different Hyflex problem domains.",
author = "Ahmed Kheiri and Ender {\"O}zcan",
year = "2013",
month = mar,
day = "22",
doi = "10.1007/978-3-642-37198-1_1",
language = "English",
isbn = "9783642371974",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "1--12",
booktitle = "Evolutionary Computation in Combinatorial Optimization",
note = "13th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2013 ; Conference date: 03-04-2013 Through 05-04-2013",
}
RIS
TY - GEN
T1 - A hyper-heuristic with a round robin neighbourhood selection
AU - Kheiri, Ahmed
AU - Özcan, Ender
PY - 2013/3/22
Y1 - 2013/3/22
N2 - An iterative selection hyper-heuristic passes a solution through a heuristic selection process to decide on a heuristic to apply from a fixed set of low level heuristics and then a move acceptance process to accept or reject the newly created solution at each step. In this study, we introduce Robinhood hyper-heuristic whose heuristic selection component allocates equal share from the overall execution time for each low level heuristic, while the move acceptance component enables partial restarts when the search process stagnates. The proposed hyper-heuristic is implemented as an extension to a public software used for benchmarking of hyper-heuristics, namely HyFlex. The empirical results indicate that Robinhood hyper-heuristic is a simple, yet powerful and general multistage algorithm performing better than most of the previously proposed selection hyper-heuristics across six different Hyflex problem domains.
AB - An iterative selection hyper-heuristic passes a solution through a heuristic selection process to decide on a heuristic to apply from a fixed set of low level heuristics and then a move acceptance process to accept or reject the newly created solution at each step. In this study, we introduce Robinhood hyper-heuristic whose heuristic selection component allocates equal share from the overall execution time for each low level heuristic, while the move acceptance component enables partial restarts when the search process stagnates. The proposed hyper-heuristic is implemented as an extension to a public software used for benchmarking of hyper-heuristics, namely HyFlex. The empirical results indicate that Robinhood hyper-heuristic is a simple, yet powerful and general multistage algorithm performing better than most of the previously proposed selection hyper-heuristics across six different Hyflex problem domains.
U2 - 10.1007/978-3-642-37198-1_1
DO - 10.1007/978-3-642-37198-1_1
M3 - Conference contribution/Paper
AN - SCOPUS:84875116819
SN - 9783642371974
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 1
EP - 12
BT - Evolutionary Computation in Combinatorial Optimization
PB - Springer
T2 - 13th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2013
Y2 - 3 April 2013 through 5 April 2013
ER -
