TY - CHAP U1 - Konferenzveröffentlichung A1 - Hagg, Alexander T1 - Hierarchical Surrogate Modeling for Illumination Algorithms T2 - GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference. Berlin, Germany, July 15-19, 2017 N2 - Evolutionary illumination is a recent technique that allows producing many diverse, optimal solutions in a map of manually defined features. To support the large amount of objective function evaluations, surrogate model assistance was recently introduced. Illumination models need to represent many more, diverse optimal regions than classical surrogate models. In this PhD thesis, we propose to decompose the sample set, decreasing model complexity, by hierarchically segmenting the training set according to their coordinates in feature space. An ensemble of diverse models can then be trained to serve as a surrogate to illumination. KW - bagging KW - surrogate modeling KW - evolutionary illumination SN - 978-1-4503-4939-0 SB - 978-1-4503-4939-0 U6 - https://doi.org/10.1145/3067695.3082495 DO - https://doi.org/10.1145/3067695.3082495 AX - 1703.09926 SP - 1407 EP - 1410 PB - ACM ER -