---
_id: '48838'
abstract:
- lang: eng
  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.'
author:
- first_name: Jakob
  full_name: Bossek, Jakob
  id: '102979'
  last_name: Bossek
  orcid: 0000-0002-4121-4668
- first_name: Bernd
  full_name: Bischl, Bernd
  last_name: Bischl
- first_name: Tobias
  full_name: Wagner, Tobias
  last_name: Wagner
- first_name: Günter
  full_name: Rudolph, Günter
  last_name: Rudolph
citation:
  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>'
  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} }'
  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>.'
  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>.'
  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>.
  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.'
date_created: 2023-11-14T15:58:51Z
date_updated: 2023-12-13T10:40:30Z
department:
- _id: '819'
doi: 10.1145/2739480.2754673
extern: '1'
keyword:
- evolutionary algorithms
- model-based optimization
- parameter tuning
language:
- iso: eng
page: 1319–1326
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference
publication_identifier:
  isbn:
  - 978-1-4503-3472-3
publication_status: published
publisher: Association for Computing Machinery
series_title: GECCO ’15
status: public
title: Learning Feature-Parameter Mappings for Parameter Tuning via the Profile Expected
  Improvement
type: conference
user_id: '102979'
year: '2015'
...