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Population-based vs. Single-solution Heuristics for the Travelling Thief Problem., and . GECCO, page 317-324. ACM, (2016)A hyperheuristic approach based on low-level heuristics for the travelling thief problem., , , , , and . Genet. Program. Evolvable Mach., 19 (1-2): 121-150 (2018)On the fitness landscapes of interdependency models in the travelling thief problem., , , , , and . GECCO Companion, page 188-191. ACM, (2022)Multi-layer local optima networks for the analysis of advanced local search-based algorithms., , , and . GECCO, page 219-227. ACM, (2020)Efficiently solving the Traveling Thief Problem using hill climbing and simulated annealing., and . Inf. Sci., (2018)MATE: A Model-Based Algorithm Tuning Engine - A Proof of Concept Towards Transparent Feature-Dependent Parameter Tuning Using Symbolic Regression., , , , , and . EvoCOP, volume 12692 of Lecture Notes in Computer Science, page 51-67. Springer, (2021)A selection hyperheuristic guided by Thompson sampling for numerical optimization., , , , , , , , and . GECCO Companion, page 1394-1402. ACM, (2021)Multi-objectiveness in the single-objective traveling thief problem., , , , and . GECCO (Companion), page 107-108. ACM, (2017)On Updating Probabilistic Graphical Models in Bayesian Optimisation Algorithm., , , , , , and . BRACIS, page 311-316. IEEE, (2019)A study of problems with multiple interdependent components - Part I.. CoRR, (2019)