A hyperheuristic approach based on low-level heuristics for the travelling thief problem

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@Article{ElYafrani:GPEM:TTP,

• author = "Mohamed {El Yafrani} and Marcella Martins and Markus Wagner and Belaid Ahiod and Myriam Delgado and Ricardo Luders",
• title = "A hyperheuristic approach based on low-level heuristics for the travelling thief problem",
• journal = "Genetic Programming and Evolvable Machines",
• year = "2018",
• volume = "19",
• number = "1-2",
• pages = "121--150",
• month = jun,
• note = "Special Issue on Automated Design and Adaptation of Heuristics for Scheduling and Combinatorial Optimisation",
• keywords = "genetic algorithms, genetic programming, Heuristic selection, Travelling thief problem, Multi-component problems",
• ISSN = "1389-2576",
• DOI = "doi:10.1007/s10710-017-9308-x",
• 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.",

}

Genetic Programming entries for Mohamed El Yafrani Marcella Scoczynski Ribeiro Martins Markus Wagner Belaid Ahiod Myriam Regattieri De Biase da Silva Delgado Ricardo Luders

Citations