@inproceedings{1cab320feabb466fb96408ec9901ad4b,
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.",
keywords = "travel salesman problem, problem domain, vehicle route problem, iterate local search, heuristic selection",
author = "Ahmed Kheiri and Ender {\"O}zcan",
year = "2013",
month = mar,
day = "20",
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 Berlin",
pages = "1--12",
editor = "Martin Middendorf and Christian Blum",
booktitle = "Evolutionary Computation in Combinatorial Optimization",
address = "Germany",
note = "13th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2013 ; Conference date: 03-04-2013 Through 05-04-2013",
}