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|Title:||A hyperheuristic approach based on low-level heuristics for the travelling thief problem|
|Author:||El Yafrani, M.|
|Citation:||Genetic Programming and Evolvable Machines, 2018; 19(1-2):121-150|
|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|
|Appears in Collections:||Computer Science publications|
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