---
_id: '48849'
abstract:
- lang: eng
  text: One-shot optimization tasks require to determine the set of solution candidates
    prior to their evaluation, i.e., without possibility for adaptive sampling. We
    consider two variants, classic one-shot optimization (where our aim is to find
    at least one solution of high quality) and one-shot regression (where the goal
    is to fit a model that resembles the true problem as well as possible). For both
    tasks it seems intuitive that well-distributed samples should perform better than
    uniform or grid-based samples, since they show a better coverage of the decision
    space. In practice, quasi-random designs such as Latin Hypercube Samples and low-discrepancy
    point sets are indeed very commonly used designs for one-shot optimization tasks.
    We study in this work how well low star discrepancy correlates with performance
    in one-shot optimization. Our results confirm an advantage of low-discrepancy
    designs, but also indicate the correlation between discrepancy values and overall
    performance is rather weak. We then demonstrate that commonly used designs may
    be far from optimal. More precisely, we evolve 24 very specific designs that each
    achieve good performance on one of our benchmark problems. Interestingly, we find
    that these specifically designed samples yield surprisingly good performance across
    the whole benchmark set. Our results therefore give strong indication that significant
    performance gains over state-of-the-art one-shot sampling techniques are possible,
    and that evolutionary algorithms can be an efficient means to evolve these.
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
- first_name: Aneta
  full_name: Neumann, Aneta
  last_name: Neumann
- first_name: Frank
  full_name: Neumann, Frank
  last_name: Neumann
citation:
  ama: 'Bossek J, Doerr C, Kerschke P, Neumann A, Neumann F. Evolving Sampling Strategies
    for One-Shot Optimization Tasks. In: <i>Parallel Problem Solving from Nature (PPSN
    XVI)</i>. Springer-Verlag; 2020:111–124. doi:<a href="https://doi.org/10.1007/978-3-030-58112-1_8">10.1007/978-3-030-58112-1_8</a>'
  apa: Bossek, J., Doerr, C., Kerschke, P., Neumann, A., &#38; Neumann, F. (2020).
    Evolving Sampling Strategies for One-Shot Optimization Tasks. <i>Parallel Problem
    Solving from Nature (PPSN XVI)</i>, 111–124. <a href="https://doi.org/10.1007/978-3-030-58112-1_8">https://doi.org/10.1007/978-3-030-58112-1_8</a>
  bibtex: '@inproceedings{Bossek_Doerr_Kerschke_Neumann_Neumann_2020, place={Berlin,
    Heidelberg}, title={Evolving Sampling Strategies for One-Shot Optimization Tasks},
    DOI={<a href="https://doi.org/10.1007/978-3-030-58112-1_8">10.1007/978-3-030-58112-1_8</a>},
    booktitle={Parallel Problem Solving from Nature (PPSN XVI)}, publisher={Springer-Verlag},
    author={Bossek, Jakob and Doerr, Carola and Kerschke, Pascal and Neumann, Aneta
    and Neumann, Frank}, year={2020}, pages={111–124} }'
  chicago: 'Bossek, Jakob, Carola Doerr, Pascal Kerschke, Aneta Neumann, and Frank
    Neumann. “Evolving Sampling Strategies for One-Shot Optimization Tasks.” In <i>Parallel
    Problem Solving from Nature (PPSN XVI)</i>, 111–124. Berlin, Heidelberg: Springer-Verlag,
    2020. <a href="https://doi.org/10.1007/978-3-030-58112-1_8">https://doi.org/10.1007/978-3-030-58112-1_8</a>.'
  ieee: 'J. Bossek, C. Doerr, P. Kerschke, A. Neumann, and F. Neumann, “Evolving Sampling
    Strategies for One-Shot Optimization Tasks,” in <i>Parallel Problem Solving from
    Nature (PPSN XVI)</i>, 2020, pp. 111–124, doi: <a href="https://doi.org/10.1007/978-3-030-58112-1_8">10.1007/978-3-030-58112-1_8</a>.'
  mla: Bossek, Jakob, et al. “Evolving Sampling Strategies for One-Shot Optimization
    Tasks.” <i>Parallel Problem Solving from Nature (PPSN XVI)</i>, Springer-Verlag,
    2020, pp. 111–124, doi:<a href="https://doi.org/10.1007/978-3-030-58112-1_8">10.1007/978-3-030-58112-1_8</a>.
  short: 'J. Bossek, C. Doerr, P. Kerschke, A. Neumann, F. Neumann, in: Parallel Problem
    Solving from Nature (PPSN XVI), Springer-Verlag, Berlin, Heidelberg, 2020, pp.
    111–124.'
date_created: 2023-11-14T15:58:53Z
date_updated: 2023-12-13T10:43:53Z
department:
- _id: '819'
doi: 10.1007/978-3-030-58112-1_8
extern: '1'
keyword:
- Continuous optimization
- Fully parallel search
- One-shot optimization
- Regression
- Surrogate-assisted optimization
language:
- iso: eng
page: 111–124
place: Berlin, Heidelberg
publication: Parallel Problem Solving from Nature (PPSN XVI)
publication_identifier:
  isbn:
  - 978-3-030-58111-4
publication_status: published
publisher: Springer-Verlag
status: public
title: Evolving Sampling Strategies for One-Shot Optimization Tasks
type: conference
user_id: '102979'
year: '2020'
...