Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/113141
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Type: Journal article
Title: A hyperheuristic approach based on low-level heuristics for the travelling thief problem
Author: El Yafrani, M.
Martins, M.
Wagner, M.
Ahiod, B.
Delgado, M.
Lüders, R.
Citation: Genetic Programming and Evolvable Machines, 2018; 19(1-2):121-150
Publisher: Springer
Issue Date: 2018
ISSN: 1389-2576
1573-7632
Statement of
Responsibility: 
Mohamed El Yafrani, Marcella Martins, Markus Wagner, Belaïd Ahiod, Myriam Delgado, Ricardo Lüders
Abstract: In this paper, we investigate the use of hyper-heuristics for the travelling thief problem (TTP). TTP is a multi-component problem, which means it has a composite structure. The problem is a combination between the travelling salesman problem and the knapsack problem. Many heuristics were proposed to deal with the two components of the problem separately. In this work, we investigate the use of automatic online heuristic selection in order to find the best combination of the different known heuristics. In order to achieve this, we propose a genetic programming based hyper-heuristic called GPHS*, and compare it to state-of-the-art algorithms. The experimental results show that the approach is competitive with those algorithms on small and mid-sized TTP instances.
Keywords: Heuristic selection; genetic programming; travelling thief problem; multi-component problems
Rights: © Springer Science+Business Media, LLC 2017
RMID: 0030073719
DOI: 10.1007/s10710-017-9308-x
Appears in Collections:Computer Science publications

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