
Generating Hard Instances for Robust Combinatorial Optimization
While research in robust optimization has attracted considerable interes...
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Empowering individual trait prediction using interactions
One component of precision medicine is to construct prediction models wi...
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Generalization of Machine Learning for Problem Reduction: A Case Study on Travelling Salesman Problems
Combinatorial optimization plays an important role in realworld problem...
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Lower Bounds on Circuit Depth of the Quantum Approximate Optimization Algorithm
The quantum approximate optimization algorithm (QAOA) is a method of app...
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Algorithm Selection for Combinatorial Search Problems: A Survey
The Algorithm Selection Problem is concerned with selecting the best alg...
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Data Combination for Problemsolving: A Case of an Open Data Exchange Platform
In recent years, rather than enclosing data within a single organization...
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Automated Algorithm Selection: Survey and Perspectives
It has long been observed that for practically any computational problem...
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A case study of algorithm selection for the traveling thief problem
Many realworld 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 wellunderstood 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 perinstance 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.
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