Details
| Original language | English |
|---|---|
| Pages (from-to) | 295-320 |
| Number of pages | 26 |
| Journal | Journal of heuristics |
| Volume | 24 |
| Issue number | 3 |
| Early online date | 7 Apr 2017 |
| Publication status | Published - Jun 2018 |
| Externally published | Yes |
Abstract
Many real-world problems are composed of several interacting components. In order to facilitate research on such interactions, the Traveling Thief Problem (TTP) was created in 2013 as the combination of two well-understood combinatorial optimization problems. With this article, we contribute in four ways. First, we create a comprehensive dataset that comprises the performance data of 21 TTP algorithms on the full original set of 9720 TTP instances. Second, we define 55 characteristics for all TPP instances that can be used to select the best algorithm on a per-instance basis. Third, we use these algorithms and features to construct the first algorithm portfolios for TTP, clearly outperforming the single best algorithm. Finally, we study which algorithms contribute most to this portfolio.
Keywords
- Algorithm portfolio, Combinatorial optimization, Instance analysis
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Computer Science(all)
- Information Systems
- Computer Science(all)
- Computer Networks and Communications
- Mathematics(all)
- Control and Optimization
- Decision Sciences(all)
- Management Science and Operations Research
- Computer Science(all)
- Artificial Intelligence
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In: Journal of heuristics, Vol. 24, No. 3, 06.2018, p. 295-320.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - A case study of algorithm selection for the traveling thief problem
AU - Wagner, Markus
AU - Lindauer, Marius
AU - Mısır, Mustafa
AU - Nallaperuma, Samadhi
AU - Hutter, Frank
N1 - Funding Information: M. Wagner was supported by the Australian Research Council (DE160100850) and by a Priority Partner Grant by the University of Adelaide, Australia. M. Lindauer and F. Hutter were supported by the DFG (German Research Foundation) under Emmy Noether Grant HU 1900/2-1. M. Mısır was supported by the Nanjing University of Aeronautics and Astronautics Starter Research Fund.
PY - 2018/6
Y1 - 2018/6
N2 - Many real-world problems are composed of several interacting components. In order to facilitate research on such interactions, the Traveling Thief Problem (TTP) was created in 2013 as the combination of two well-understood combinatorial optimization problems. With this article, we contribute in four ways. First, we create a comprehensive dataset that comprises the performance data of 21 TTP algorithms on the full original set of 9720 TTP instances. Second, we define 55 characteristics for all TPP instances that can be used to select the best algorithm on a per-instance basis. Third, we use these algorithms and features to construct the first algorithm portfolios for TTP, clearly outperforming the single best algorithm. Finally, we study which algorithms contribute most to this portfolio.
AB - Many real-world problems are composed of several interacting components. In order to facilitate research on such interactions, the Traveling Thief Problem (TTP) was created in 2013 as the combination of two well-understood combinatorial optimization problems. With this article, we contribute in four ways. First, we create a comprehensive dataset that comprises the performance data of 21 TTP algorithms on the full original set of 9720 TTP instances. Second, we define 55 characteristics for all TPP instances that can be used to select the best algorithm on a per-instance basis. Third, we use these algorithms and features to construct the first algorithm portfolios for TTP, clearly outperforming the single best algorithm. Finally, we study which algorithms contribute most to this portfolio.
KW - Algorithm portfolio
KW - Combinatorial optimization
KW - Instance analysis
UR - http://www.scopus.com/inward/record.url?scp=85017099292&partnerID=8YFLogxK
U2 - 10.1007/s10732-017-9328-y
DO - 10.1007/s10732-017-9328-y
M3 - Article
AN - SCOPUS:85017099292
VL - 24
SP - 295
EP - 320
JO - Journal of heuristics
JF - Journal of heuristics
SN - 1381-1231
IS - 3
ER -