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Enabling high-dimensional surrogate-assisted optimization by using sliding windows.

, , and . GECCO (Companion), page 1630-1637. ACM, (2017)

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Dynamic Fitness Landscape Analysis., , and . EUROCAST, volume 13789 of Lecture Notes in Computer Science, page 78-86. Springer, (2022)Dynamic Vehicle Routing with Time-Linkage: From Problem States to Algorithm Performance., , , , and . EUROCAST, volume 13789 of Lecture Notes in Computer Science, page 69-77. Springer, (2022)Analysis and Handling of Dynamic Problem Changes in Open-Ended Optimization., , , , and . EUROCAST, volume 13789 of Lecture Notes in Computer Science, page 61-68. Springer, (2022)A New Acquisition Function for Multi-objective Bayesian Optimization: Correlated Probability of Improvement., , , and . GECCO Companion, page 2308-2317. ACM, (2023)Walking through the Quadratic Assignment-Instance Space: Algorithm Performance and Landscape Measures., , , , , and . GECCO Companion, page 2108-2114. ACM, (2023)A Novel Benchmark Environment for Dynamic Factory Crane Scheduling., , , , and . ISM, volume 217 of Procedia Computer Science, page 1217-1224. Elsevier, (2023)Fitness Landscape Analysis on Binary Dynamic Optimization Problems., , , , and . ISM, volume 200 of Procedia Computer Science, page 1004-1013. Elsevier, (2021)Dynamic Warehouse Environments for Crane Stacking and Scheduling., , , , and . ISM, volume 200 of Procedia Computer Science, page 1461-1470. Elsevier, (2021)Asynchronous surrogate-assisted optimization networks., , , , and . GECCO (Companion), page 1266-1267. ACM, (2018)Surrogate-assisted microscopic traffic simulation-based optimisation of routing parameters., , , , and . Int. J. Simul. Process. Model., 14 (3): 223-233 (2019)