%0 Conference Paper %1 79e3d18505f24f76ba83428f90f390a3 %A Dietrich, Konstantin %A Vermetten, Diederick %A Doerr, Carola %A Kerschke, Pascal %B Proceedings of the Genetic and Evolutionary Computation Conference %C United States of America %D 2024 %I Association for Computing Machinery (ACM) %K topic_engineering FIS_scads imported %P 1007 -- 1016 %R 10.1145/3638529.3654100 %T Impact of Training Instance Selection on Automated Algorithm Selection Models for Numerical Black-box Optimization %X The recently proposed MA-BBOB function generator provides a way to create numerical black-box benchmark problems based on the well-established BBOB suite. Initial studies on this generator highlighted its ability to smoothly transition between the component functions, both from a low-level landscape feature perspective, as well as with regard to algorithm performance. This suggests that MA-BBOB-generated functions can be an ideal testbed for automated machine learning methods, such as automated algorithm selection (AAS). In this paper, we generate 11800 functions in dimensions $d=2$ and $d=5$, respectively, and analyze the potential gains from AAS by studying performance complementarity within a set of eight algorithms. We combine this performance data with exploratory landscape features to create an AAS pipeline that we use to investigate how to efficiently select training sets within this space. We show that simply using the BBOB component functions for training yields poor test performance, while the ranking between uniformly chosen and diversity-based training sets strongly depends on the distribution of the test set.

@inproceedings{79e3d18505f24f76ba83428f90f390a3, abstract = {The recently proposed MA-BBOB function generator provides a way to create numerical black-box benchmark problems based on the well-established BBOB suite. Initial studies on this generator highlighted its ability to smoothly transition between the component functions, both from a low-level landscape feature perspective, as well as with regard to algorithm performance. This suggests that MA-BBOB-generated functions can be an ideal testbed for automated machine learning methods, such as automated algorithm selection (AAS). In this paper, we generate 11800 functions in dimensions $d=2$ and $d=5$, respectively, and analyze the potential gains from AAS by studying performance complementarity within a set of eight algorithms. We combine this performance data with exploratory landscape features to create an AAS pipeline that we use to investigate how to efficiently select training sets within this space. We show that simply using the BBOB component functions for training yields poor test performance, while the ranking between uniformly chosen and diversity-based training sets strongly depends on the distribution of the test set.}, added-at = {2024-11-28T16:27:18.000+0100}, address = {United States of America}, author = {Dietrich, Konstantin and Vermetten, Diederick and Doerr, Carola and Kerschke, Pascal}, biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/2d43e3802463fa8a8eb863f7a9e2c7fd1/scadsfct}, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference}, day = 14, doi = {10.1145/3638529.3654100}, interhash = {7b024f3c70650fb5887b578287f4babb}, intrahash = {d43e3802463fa8a8eb863f7a9e2c7fd1}, keywords = {topic_engineering FIS_scads imported}, language = {English}, month = jul, note = {Publisher Copyright: {\textcopyright} 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.}, pages = {1007 -- 1016}, publisher = {Association for Computing Machinery (ACM)}, timestamp = {2024-11-28T17:41:02.000+0100}, title = {Impact of Training Instance Selection on Automated Algorithm Selection Models for Numerical Black-box Optimization}, year = 2024 }