Polynomial time approximation schemes for Euclidean traveling salesman and other geometric problems
Journal of the ACM (JACM)
Approximation algorithms
Solution of a Min-Max Vehicle Routing Problem
INFORMS Journal on Computing
Evolving combinatorial problem instances that are difficult to solve
Evolutionary Computation
SATzilla: portfolio-based algorithm selection for SAT
Journal of Artificial Intelligence Research
Understanding TSP difficulty by learning from evolved instances
LION'10 Proceedings of the 4th international conference on Learning and intelligent optimization
Bioinspired Computation in Combinatorial Optimization: Algorithms and Their Computational Complexity
Bioinspired Computation in Combinatorial Optimization: Algorithms and Their Computational Complexity
Review: Measuring instance difficulty for combinatorial optimization problems
Computers and Operations Research
Algorithm selection based on exploratory landscape analysis and cost-sensitive learning
Proceedings of the 14th annual conference on Genetic and evolutionary computation
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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Understanding the behaviour of well-known algorithms for classical NP-hard optimisation problems is still a difficult task. With this paper, we contribute to this research direction and carry out a feature based comparison of local search and the well-known Christofides approximation algorithm for the Traveling Salesperson Problem. We use an evolutionary algorithm approach to construct easy and hard instances for the Christofides algorithm, where we measure hardness in terms of approximation ratio. Our results point out important features and lead to hard and easy instances for this famous algorithm. Furthermore, our cross-comparison gives new insights on the complementary benefits of the different approaches.