---
_id: '48850'
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.
author:
- first_name: Jakob
  full_name: Bossek, Jakob
  id: '102979'
  last_name: Bossek
  orcid: 0000-0002-4121-4668
- first_name: Carola
  full_name: Doerr, Carola
  last_name: Doerr
- first_name: Pascal
  full_name: Kerschke, Pascal
  last_name: Kerschke
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>'
  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>'
  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} }'
  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>.'
  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>.'
  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.'
date_created: 2023-11-14T15:58:53Z
date_updated: 2023-12-13T10:44:01Z
department:
- _id: '819'
doi: 10.1145/3377930.3390155
extern: '1'
keyword:
- continuous black-box optimization
- design of experiments
- initial design
- sequential model-based optimization
language:
- iso: eng
page: 778–786
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference
publication_identifier:
  isbn:
  - 978-1-4503-7128-5
publication_status: published
publisher: Association for Computing Machinery
series_title: GECCO ’20
status: public
title: 'Initial Design Strategies and Their Effects on Sequential Model-Based Optimization:
  An Exploratory Case Study Based on BBOB'
type: conference
user_id: '102979'
year: '2020'
...