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Evolving Instances for Maximizing Performance Differences of State-of-the-Art Inexact TSP Solvers., and . LION, volume 10079 of Lecture Notes in Computer Science, page 48-59. Springer, (2016)Multi-objective Performance Measurement: Alternatives to PAR10 and Expected Running Time., and . LION, volume 11353 of Lecture Notes in Computer Science, page 215-219. Springer, (2018)Local Search and the Traveling Salesman Problem: A Feature-Based Characterization of Problem Hardness., , , , , and . LION, volume 7219 of Lecture Notes in Computer Science, page 115-129. Springer, (2012)Evolving diverse TSP instances by means of novel and creative mutation operators., , , , , and . FOGA, page 58-71. ACM, (2019)Local search effects in bi-objective orienteering., , , , and . GECCO, page 585-592. ACM, (2018)Parameterization of state-of-the-art performance indicators: a robustness study based on inexact TSP solvers., , and . GECCO (Companion), page 1737-1744. ACM, (2018)Learning Feature-Parameter Mappings for Parameter Tuning via the Profile Expected Improvement., , , and . GECCO, page 1319-1326. ACM, (2015)Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic Routing of a Vehicle., , , , , and . GECCO, page 425-432. ACM, (2015)Benchmarking in Optimization: Best Practice and Open Issues., , , , , , , , , and 5 other author(s). CoRR, (2020)A novel feature-based approach to characterize algorithm performance for the traveling salesperson problem., , , , , and . Ann. Math. Artif. Intell., 69 (2): 151-182 (2013)