[{"publication_status":"published","publication_identifier":{"isbn":["978-1-4503-7128-5"]},"citation":{"ama":"Bossek J, Doerr C, Kerschke P. Initial Design Strategies and Their Effects on Sequential Model-Based Optimization: An Exploratory Case Study Based on BBOB. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>. GECCO ’20. Association for Computing Machinery; 2020:778–786. doi:<a href=\"https://doi.org/10.1145/3377930.3390155\">10.1145/3377930.3390155</a>","ieee":"J. Bossek, C. Doerr, and P. Kerschke, “Initial Design Strategies and Their Effects on Sequential Model-Based Optimization: An Exploratory Case Study Based on BBOB,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 2020, pp. 778–786, doi: <a href=\"https://doi.org/10.1145/3377930.3390155\">10.1145/3377930.3390155</a>.","chicago":"Bossek, Jakob, Carola Doerr, and Pascal Kerschke. “Initial Design Strategies and Their Effects on Sequential Model-Based Optimization: An Exploratory Case Study Based on BBOB.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 778–786. GECCO ’20. New York, NY, USA: Association for Computing Machinery, 2020. <a href=\"https://doi.org/10.1145/3377930.3390155\">https://doi.org/10.1145/3377930.3390155</a>.","bibtex":"@inproceedings{Bossek_Doerr_Kerschke_2020, place={New York, NY, USA}, series={GECCO ’20}, title={Initial Design Strategies and Their Effects on Sequential Model-Based Optimization: An Exploratory Case Study Based on BBOB}, DOI={<a href=\"https://doi.org/10.1145/3377930.3390155\">10.1145/3377930.3390155</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference}, publisher={Association for Computing Machinery}, author={Bossek, Jakob and Doerr, Carola and Kerschke, Pascal}, year={2020}, pages={778–786}, collection={GECCO ’20} }","mla":"Bossek, Jakob, et al. “Initial Design Strategies and Their Effects on Sequential Model-Based Optimization: An Exploratory Case Study Based on BBOB.” <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, Association for Computing Machinery, 2020, pp. 778–786, doi:<a href=\"https://doi.org/10.1145/3377930.3390155\">10.1145/3377930.3390155</a>.","short":"J. Bossek, C. Doerr, P. Kerschke, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2020, pp. 778–786.","apa":"Bossek, J., Doerr, C., &#38; Kerschke, P. (2020). Initial Design Strategies and Their Effects on Sequential Model-Based Optimization: An Exploratory Case Study Based on BBOB. <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 778–786. <a href=\"https://doi.org/10.1145/3377930.3390155\">https://doi.org/10.1145/3377930.3390155</a>"},"page":"778–786","place":"New York, NY, USA","author":[{"id":"102979","full_name":"Bossek, Jakob","orcid":"0000-0002-4121-4668","last_name":"Bossek","first_name":"Jakob"},{"first_name":"Carola","full_name":"Doerr, Carola","last_name":"Doerr"},{"full_name":"Kerschke, Pascal","last_name":"Kerschke","first_name":"Pascal"}],"date_updated":"2023-12-13T10:44:01Z","doi":"10.1145/3377930.3390155","type":"conference","status":"public","user_id":"102979","series_title":"GECCO ’20","department":[{"_id":"819"}],"_id":"48850","extern":"1","year":"2020","date_created":"2023-11-14T15:58:53Z","publisher":"Association for Computing Machinery","title":"Initial Design Strategies and Their Effects on Sequential Model-Based Optimization: An Exploratory Case Study Based on BBOB","publication":"Proceedings of the Genetic and Evolutionary Computation Conference","abstract":[{"lang":"eng","text":"Sequential model-based optimization (SMBO) approaches are algorithms for solving problems that require computationally or otherwise expensive function evaluations. The key design principle of SMBO is a substitution of the true objective function by a surrogate, which is used to propose the point(s) to be evaluated next. SMBO algorithms are intrinsically modular, leaving the user with many important design choices. Significant research efforts go into understanding which settings perform best for which type of problems. Most works, however, focus on the choice of the model, the acquisition function, and the strategy used to optimize the latter. The choice of the initial sampling strategy, however, receives much less attention. Not surprisingly, quite diverging recommendations can be found in the literature. We analyze in this work how the size and the distribution of the initial sample influences the overall quality of the efficient global optimization (EGO) algorithm, a well-known SMBO approach. While, overall, small initial budgets using Halton sampling seem preferable, we also observe that the performance landscape is rather unstructured. We furthermore identify several situations in which EGO performs unfavorably against random sampling. Both observations indicate that an adaptive SMBO design could be beneficial, making SMBO an interesting test-bed for automated algorithm design."}],"language":[{"iso":"eng"}],"keyword":["continuous black-box optimization","design of experiments","initial design","sequential model-based optimization"]},{"page":"1319–1326","citation":{"apa":"Bossek, J., Bischl, B., Wagner, T., &#38; Rudolph, G. (2015). Learning Feature-Parameter Mappings for Parameter Tuning via the Profile Expected Improvement. <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 1319–1326. <a href=\"https://doi.org/10.1145/2739480.2754673\">https://doi.org/10.1145/2739480.2754673</a>","bibtex":"@inproceedings{Bossek_Bischl_Wagner_Rudolph_2015, place={New York, NY, USA}, series={GECCO ’15}, title={Learning Feature-Parameter Mappings for Parameter Tuning via the Profile Expected Improvement}, DOI={<a href=\"https://doi.org/10.1145/2739480.2754673\">10.1145/2739480.2754673</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference}, publisher={Association for Computing Machinery}, author={Bossek, Jakob and Bischl, Bernd and Wagner, Tobias and Rudolph, Günter}, year={2015}, pages={1319–1326}, collection={GECCO ’15} }","short":"J. Bossek, B. Bischl, T. Wagner, G. Rudolph, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2015, pp. 1319–1326.","mla":"Bossek, Jakob, et al. “Learning Feature-Parameter Mappings for Parameter Tuning via the Profile Expected Improvement.” <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, Association for Computing Machinery, 2015, pp. 1319–1326, doi:<a href=\"https://doi.org/10.1145/2739480.2754673\">10.1145/2739480.2754673</a>.","ama":"Bossek J, Bischl B, Wagner T, Rudolph G. Learning Feature-Parameter Mappings for Parameter Tuning via the Profile Expected Improvement. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>. GECCO ’15. Association for Computing Machinery; 2015:1319–1326. doi:<a href=\"https://doi.org/10.1145/2739480.2754673\">10.1145/2739480.2754673</a>","ieee":"J. Bossek, B. Bischl, T. Wagner, and G. Rudolph, “Learning Feature-Parameter Mappings for Parameter Tuning via the Profile Expected Improvement,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 2015, pp. 1319–1326, doi: <a href=\"https://doi.org/10.1145/2739480.2754673\">10.1145/2739480.2754673</a>.","chicago":"Bossek, Jakob, Bernd Bischl, Tobias Wagner, and Günter Rudolph. “Learning Feature-Parameter Mappings for Parameter Tuning via the Profile Expected Improvement.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 1319–1326. GECCO ’15. New York, NY, USA: Association for Computing Machinery, 2015. <a href=\"https://doi.org/10.1145/2739480.2754673\">https://doi.org/10.1145/2739480.2754673</a>."},"year":"2015","place":"New York, NY, USA","publication_identifier":{"isbn":["978-1-4503-3472-3"]},"publication_status":"published","doi":"10.1145/2739480.2754673","title":"Learning Feature-Parameter Mappings for Parameter Tuning via the Profile Expected Improvement","author":[{"last_name":"Bossek","orcid":"0000-0002-4121-4668","full_name":"Bossek, Jakob","id":"102979","first_name":"Jakob"},{"first_name":"Bernd","full_name":"Bischl, Bernd","last_name":"Bischl"},{"full_name":"Wagner, Tobias","last_name":"Wagner","first_name":"Tobias"},{"full_name":"Rudolph, Günter","last_name":"Rudolph","first_name":"Günter"}],"date_created":"2023-11-14T15:58:51Z","publisher":"Association for Computing Machinery","date_updated":"2023-12-13T10:40:30Z","status":"public","abstract":[{"text":"The majority of algorithms can be controlled or adjusted by parameters. Their values can substantially affect the algorithms’ performance. Since the manual exploration of the parameter space is tedious – even for few parameters – several automatic procedures for parameter tuning have been proposed. Recent approaches also take into account some characteristic properties of the problem instances, frequently termed instance features. Our contribution is the proposal of a novel concept for feature-based algorithm parameter tuning, which applies an approximating surrogate model for learning the continuous feature-parameter mapping. To accomplish this, we learn a joint model of the algorithm performance based on both the algorithm parameters and the instance features. The required data is gathered using a recently proposed acquisition function for model refinement in surrogate-based optimization: the profile expected improvement. This function provides an avenue for maximizing the information required for the feature-parameter mapping, i.e., the mapping from instance features to the corresponding optimal algorithm parameters. The approach is validated by applying the tuner to exemplary evolutionary algorithms and problems, for which theoretically grounded or heuristically determined feature-parameter mappings are available.","lang":"eng"}],"publication":"Proceedings of the Genetic and Evolutionary Computation Conference","type":"conference","extern":"1","language":[{"iso":"eng"}],"keyword":["evolutionary algorithms","model-based optimization","parameter tuning"],"department":[{"_id":"819"}],"user_id":"102979","series_title":"GECCO ’15","_id":"48838"}]