---
_id: '54548'
author:
- first_name: Raphael Patrick
  full_name: Prager, Raphael Patrick
  last_name: Prager
- first_name: Heike
  full_name: Trautmann, Heike
  id: '100740'
  last_name: Trautmann
  orcid: 0000-0002-9788-8282
citation:
  ama: Prager RP, Trautmann H. Exploratory Landscape Analysis for Mixed-Variable Problems.
    <i>IEEE Transactions on Evolutionary Computation</i>. Published online 2024:1-1.
    doi:<a href="https://doi.org/10.1109/TEVC.2024.3399560">10.1109/TEVC.2024.3399560</a>
  apa: Prager, R. P., &#38; Trautmann, H. (2024). Exploratory Landscape Analysis for
    Mixed-Variable Problems. <i>IEEE Transactions on Evolutionary Computation</i>,
    1–1. <a href="https://doi.org/10.1109/TEVC.2024.3399560">https://doi.org/10.1109/TEVC.2024.3399560</a>
  bibtex: '@article{Prager_Trautmann_2024, title={Exploratory Landscape Analysis for
    Mixed-Variable Problems}, DOI={<a href="https://doi.org/10.1109/TEVC.2024.3399560">10.1109/TEVC.2024.3399560</a>},
    journal={IEEE Transactions on Evolutionary Computation}, author={Prager, Raphael
    Patrick and Trautmann, Heike}, year={2024}, pages={1–1} }'
  chicago: Prager, Raphael Patrick, and Heike Trautmann. “Exploratory Landscape Analysis
    for Mixed-Variable Problems.” <i>IEEE Transactions on Evolutionary Computation</i>,
    2024, 1–1. <a href="https://doi.org/10.1109/TEVC.2024.3399560">https://doi.org/10.1109/TEVC.2024.3399560</a>.
  ieee: 'R. P. Prager and H. Trautmann, “Exploratory Landscape Analysis for Mixed-Variable
    Problems,” <i>IEEE Transactions on Evolutionary Computation</i>, pp. 1–1, 2024,
    doi: <a href="https://doi.org/10.1109/TEVC.2024.3399560">10.1109/TEVC.2024.3399560</a>.'
  mla: Prager, Raphael Patrick, and Heike Trautmann. “Exploratory Landscape Analysis
    for Mixed-Variable Problems.” <i>IEEE Transactions on Evolutionary Computation</i>,
    2024, pp. 1–1, doi:<a href="https://doi.org/10.1109/TEVC.2024.3399560">10.1109/TEVC.2024.3399560</a>.
  short: R.P. Prager, H. Trautmann, IEEE Transactions on Evolutionary Computation
    (2024) 1–1.
date_created: 2024-06-03T06:16:33Z
date_updated: 2024-06-03T06:17:13Z
department:
- _id: '819'
doi: 10.1109/TEVC.2024.3399560
keyword:
- Optimization
- Evolutionary computation
- Benchmark testing
- Hyperparameter optimization
- Portfolios
- Extraterrestrial measurements
- Dispersion
- Exploratory landscape analysis
- mixed-variable problem
- mixed search spaces
- automated algorithm selection
language:
- iso: eng
page: 1-1
publication: IEEE Transactions on Evolutionary Computation
status: public
title: Exploratory Landscape Analysis for Mixed-Variable Problems
type: journal_article
user_id: '15504'
year: '2024'
...
---
_id: '47522'
abstract:
- lang: eng
  text: Artificial benchmark functions are commonly used in optimization research
    because of their ability to rapidly evaluate potential solutions, making them
    a preferred substitute for real-world problems. However, these benchmark functions
    have faced criticism for their limited resemblance to real-world problems. In
    response, recent research has focused on automatically generating new benchmark
    functions for areas where established test suites are inadequate. These approaches
    have limitations, such as the difficulty of generating new benchmark functions
    that exhibit exploratory landscape analysis (ELA) features beyond those of existing
    benchmarks.The objective of this work is to develop a method for generating benchmark
    functions for single-objective continuous optimization with user-specified structural
    properties. Specifically, we aim to demonstrate a proof of concept for a method
    that uses an ELA feature vector to specify these properties in advance. To achieve
    this, we begin by generating a random sample of decision space variables and objective
    values. We then adjust the objective values using CMA-ES until the corresponding
    features of our new problem match the predefined ELA features within a specified
    threshold. By iteratively transforming the landscape in this way, we ensure that
    the resulting function exhibits the desired properties. To create the final function,
    we use the resulting point cloud as training data for a simple neural network
    that produces a function exhibiting the target ELA features. We demonstrate the
    effectiveness of this approach by replicating the existing functions of the well-known
    BBOB suite and creating new functions with ELA feature values that are not present
    in BBOB.
author:
- first_name: Raphael Patrick
  full_name: Prager, Raphael Patrick
  last_name: Prager
- first_name: Konstantin
  full_name: Dietrich, Konstantin
  last_name: Dietrich
- first_name: Lennart
  full_name: Schneider, Lennart
  last_name: Schneider
- first_name: Lennart
  full_name: Schäpermeier, Lennart
  last_name: Schäpermeier
- first_name: Bernd
  full_name: Bischl, Bernd
  last_name: Bischl
- first_name: Pascal
  full_name: Kerschke, Pascal
  last_name: Kerschke
- first_name: Heike
  full_name: Trautmann, Heike
  id: '100740'
  last_name: Trautmann
  orcid: 0000-0002-9788-8282
- first_name: Olaf
  full_name: Mersmann, Olaf
  last_name: Mersmann
citation:
  ama: 'Prager RP, Dietrich K, Schneider L, et al. Neural Networks as Black-Box Benchmark
    Functions Optimized for Exploratory Landscape Features. In: <i>Proceedings of
    the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>. FOGA
    ’23. Association for Computing Machinery; 2023:129–139. doi:<a href="https://doi.org/10.1145/3594805.3607136">10.1145/3594805.3607136</a>'
  apa: Prager, R. P., Dietrich, K., Schneider, L., Schäpermeier, L., Bischl, B., Kerschke,
    P., Trautmann, H., &#38; Mersmann, O. (2023). Neural Networks as Black-Box Benchmark
    Functions Optimized for Exploratory Landscape Features. <i>Proceedings of the
    17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>, 129–139.
    <a href="https://doi.org/10.1145/3594805.3607136">https://doi.org/10.1145/3594805.3607136</a>
  bibtex: '@inproceedings{Prager_Dietrich_Schneider_Schäpermeier_Bischl_Kerschke_Trautmann_Mersmann_2023,
    place={New York, NY, USA}, series={FOGA ’23}, title={Neural Networks as Black-Box
    Benchmark Functions Optimized for Exploratory Landscape Features}, DOI={<a href="https://doi.org/10.1145/3594805.3607136">10.1145/3594805.3607136</a>},
    booktitle={Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic
    Algorithms}, publisher={Association for Computing Machinery}, author={Prager,
    Raphael Patrick and Dietrich, Konstantin and Schneider, Lennart and Schäpermeier,
    Lennart and Bischl, Bernd and Kerschke, Pascal and Trautmann, Heike and Mersmann,
    Olaf}, year={2023}, pages={129–139}, collection={FOGA ’23} }'
  chicago: 'Prager, Raphael Patrick, Konstantin Dietrich, Lennart Schneider, Lennart
    Schäpermeier, Bernd Bischl, Pascal Kerschke, Heike Trautmann, and Olaf Mersmann.
    “Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape
    Features.” In <i>Proceedings of the 17th ACM/SIGEVO Conference on Foundations
    of Genetic Algorithms</i>, 129–139. FOGA ’23. New York, NY, USA: Association for
    Computing Machinery, 2023. <a href="https://doi.org/10.1145/3594805.3607136">https://doi.org/10.1145/3594805.3607136</a>.'
  ieee: 'R. P. Prager <i>et al.</i>, “Neural Networks as Black-Box Benchmark Functions
    Optimized for Exploratory Landscape Features,” in <i>Proceedings of the 17th ACM/SIGEVO
    Conference on Foundations of Genetic Algorithms</i>, 2023, pp. 129–139, doi: <a
    href="https://doi.org/10.1145/3594805.3607136">10.1145/3594805.3607136</a>.'
  mla: Prager, Raphael Patrick, et al. “Neural Networks as Black-Box Benchmark Functions
    Optimized for Exploratory Landscape Features.” <i>Proceedings of the 17th ACM/SIGEVO
    Conference on Foundations of Genetic Algorithms</i>, Association for Computing
    Machinery, 2023, pp. 129–139, doi:<a href="https://doi.org/10.1145/3594805.3607136">10.1145/3594805.3607136</a>.
  short: 'R.P. Prager, K. Dietrich, L. Schneider, L. Schäpermeier, B. Bischl, P. Kerschke,
    H. Trautmann, O. Mersmann, in: Proceedings of the 17th ACM/SIGEVO Conference on
    Foundations of Genetic Algorithms, Association for Computing Machinery, New York,
    NY, USA, 2023, pp. 129–139.'
date_created: 2023-09-27T15:43:17Z
date_updated: 2023-10-16T12:33:02Z
department:
- _id: '34'
- _id: '819'
doi: 10.1145/3594805.3607136
keyword:
- Benchmarking
- Instance Generator
- Black-Box Continuous Optimization
- Exploratory Landscape Analysis
- Neural Networks
language:
- iso: eng
page: 129–139
place: New York, NY, USA
publication: Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic
  Algorithms
publication_identifier:
  isbn:
  - '9798400702020'
publisher: Association for Computing Machinery
series_title: FOGA ’23
status: public
title: Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory
  Landscape Features
type: conference
user_id: '15504'
year: '2023'
...
---
_id: '46396'
abstract:
- lang: eng
  text: The steady supply of new optimization methods makes the algorithm selection
    problem (ASP) an increasingly pressing and challenging task, specially for real-world
    black-box optimization problems. The introduced approach considers the ASP as
    a cost-sensitive classification task which is based on Exploratory Landscape Analysis.
    Low-level features gathered by systematic sampling of the function on the feasible
    set are used to predict a well-performing algorithm out of a given portfolio.
    Example-specific label costs are defined by the expected runtime of each candidate
    algorithm. We use one-sided support vector regression to solve this learning problem.
    The approach is illustrated by means of the optimization problems and algorithms
    of the BBOB’09/10 workshop.
author:
- first_name: Bernd
  full_name: Bischl, Bernd
  last_name: Bischl
- first_name: Olaf
  full_name: Mersmann, Olaf
  last_name: Mersmann
- first_name: Heike
  full_name: Trautmann, Heike
  id: '100740'
  last_name: Trautmann
  orcid: 0000-0002-9788-8282
- first_name: Mike
  full_name: Preuß, Mike
  last_name: Preuß
citation:
  ama: 'Bischl B, Mersmann O, Trautmann H, Preuß M. Algorithm Selection Based on Exploratory
    Landscape Analysis and Cost-Sensitive Learning. In: <i>Proceedings of the 14th
    Annual Conference on Genetic and Evolutionary Computation</i>. GECCO ’12. Association
    for Computing Machinery; 2012:313–320. doi:<a href="https://doi.org/10.1145/2330163.2330209">10.1145/2330163.2330209</a>'
  apa: Bischl, B., Mersmann, O., Trautmann, H., &#38; Preuß, M. (2012). Algorithm
    Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning.
    <i>Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation</i>,
    313–320. <a href="https://doi.org/10.1145/2330163.2330209">https://doi.org/10.1145/2330163.2330209</a>
  bibtex: '@inproceedings{Bischl_Mersmann_Trautmann_Preuß_2012, place={New York, NY,
    USA}, series={GECCO ’12}, title={Algorithm Selection Based on Exploratory Landscape
    Analysis and Cost-Sensitive Learning}, DOI={<a href="https://doi.org/10.1145/2330163.2330209">10.1145/2330163.2330209</a>},
    booktitle={Proceedings of the 14th Annual Conference on Genetic and Evolutionary
    Computation}, publisher={Association for Computing Machinery}, author={Bischl,
    Bernd and Mersmann, Olaf and Trautmann, Heike and Preuß, Mike}, year={2012}, pages={313–320},
    collection={GECCO ’12} }'
  chicago: 'Bischl, Bernd, Olaf Mersmann, Heike Trautmann, and Mike Preuß. “Algorithm
    Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning.”
    In <i>Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation</i>,
    313–320. GECCO ’12. New York, NY, USA: Association for Computing Machinery, 2012.
    <a href="https://doi.org/10.1145/2330163.2330209">https://doi.org/10.1145/2330163.2330209</a>.'
  ieee: 'B. Bischl, O. Mersmann, H. Trautmann, and M. Preuß, “Algorithm Selection
    Based on Exploratory Landscape Analysis and Cost-Sensitive Learning,” in <i>Proceedings
    of the 14th Annual Conference on Genetic and Evolutionary Computation</i>, 2012,
    pp. 313–320, doi: <a href="https://doi.org/10.1145/2330163.2330209">10.1145/2330163.2330209</a>.'
  mla: Bischl, Bernd, et al. “Algorithm Selection Based on Exploratory Landscape Analysis
    and Cost-Sensitive Learning.” <i>Proceedings of the 14th Annual Conference on
    Genetic and Evolutionary Computation</i>, Association for Computing Machinery,
    2012, pp. 313–320, doi:<a href="https://doi.org/10.1145/2330163.2330209">10.1145/2330163.2330209</a>.
  short: 'B. Bischl, O. Mersmann, H. Trautmann, M. Preuß, in: Proceedings of the 14th
    Annual Conference on Genetic and Evolutionary Computation, Association for Computing
    Machinery, New York, NY, USA, 2012, pp. 313–320.'
date_created: 2023-08-04T15:51:56Z
date_updated: 2023-10-16T13:48:48Z
department:
- _id: '34'
- _id: '819'
doi: 10.1145/2330163.2330209
keyword:
- machine learning
- exploratory landscape analysis
- fitness landscape
- benchmarking
- evolutionary optimization
- bbob test set
- algorithm selection
language:
- iso: eng
page: 313–320
place: New York, NY, USA
publication: Proceedings of the 14th Annual Conference on Genetic and Evolutionary
  Computation
publication_identifier:
  isbn:
  - '9781450311779'
publisher: Association for Computing Machinery
series_title: GECCO ’12
status: public
title: Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive
  Learning
type: conference
user_id: '15504'
year: '2012'
...
---
_id: '46401'
abstract:
- lang: eng
  text: Exploratory Landscape Analysis subsumes a number of techniques employed to
    obtain knowledge about the properties of an unknown optimization problem, especially
    insofar as these properties are important for the performance of optimization
    algorithms. Where in a first attempt, one could rely on high-level features designed
    by experts, we approach the problem from a different angle here, namely by using
    relatively cheap low-level computer generated features. Interestingly, very few
    features are needed to separate the BBOB problem groups and also for relating
    a problem to high-level, expert designed features, paving the way for automatic
    algorithm selection.
author:
- first_name: Olaf
  full_name: Mersmann, Olaf
  last_name: Mersmann
- first_name: Bernd
  full_name: Bischl, Bernd
  last_name: Bischl
- first_name: Heike
  full_name: Trautmann, Heike
  id: '100740'
  last_name: Trautmann
  orcid: 0000-0002-9788-8282
- first_name: Mike
  full_name: Preuss, Mike
  last_name: Preuss
- first_name: Claus
  full_name: Weihs, Claus
  last_name: Weihs
- first_name: Günter
  full_name: Rudolph, Günter
  last_name: Rudolph
citation:
  ama: 'Mersmann O, Bischl B, Trautmann H, Preuss M, Weihs C, Rudolph G. Exploratory
    Landscape Analysis. In: <i>Proceedings of the 13th Annual Conference on Genetic
    and Evolutionary Computation</i>. GECCO ’11. Association for Computing Machinery;
    2011:829–836. doi:<a href="https://doi.org/10.1145/2001576.2001690">10.1145/2001576.2001690</a>'
  apa: Mersmann, O., Bischl, B., Trautmann, H., Preuss, M., Weihs, C., &#38; Rudolph,
    G. (2011). Exploratory Landscape Analysis. <i>Proceedings of the 13th Annual Conference
    on Genetic and Evolutionary Computation</i>, 829–836. <a href="https://doi.org/10.1145/2001576.2001690">https://doi.org/10.1145/2001576.2001690</a>
  bibtex: '@inproceedings{Mersmann_Bischl_Trautmann_Preuss_Weihs_Rudolph_2011, place={New
    York, NY, USA}, series={GECCO ’11}, title={Exploratory Landscape Analysis}, DOI={<a
    href="https://doi.org/10.1145/2001576.2001690">10.1145/2001576.2001690</a>}, booktitle={Proceedings
    of the 13th Annual Conference on Genetic and Evolutionary Computation}, publisher={Association
    for Computing Machinery}, author={Mersmann, Olaf and Bischl, Bernd and Trautmann,
    Heike and Preuss, Mike and Weihs, Claus and Rudolph, Günter}, year={2011}, pages={829–836},
    collection={GECCO ’11} }'
  chicago: 'Mersmann, Olaf, Bernd Bischl, Heike Trautmann, Mike Preuss, Claus Weihs,
    and Günter Rudolph. “Exploratory Landscape Analysis.” In <i>Proceedings of the
    13th Annual Conference on Genetic and Evolutionary Computation</i>, 829–836. GECCO
    ’11. New York, NY, USA: Association for Computing Machinery, 2011. <a href="https://doi.org/10.1145/2001576.2001690">https://doi.org/10.1145/2001576.2001690</a>.'
  ieee: 'O. Mersmann, B. Bischl, H. Trautmann, M. Preuss, C. Weihs, and G. Rudolph,
    “Exploratory Landscape Analysis,” in <i>Proceedings of the 13th Annual Conference
    on Genetic and Evolutionary Computation</i>, 2011, pp. 829–836, doi: <a href="https://doi.org/10.1145/2001576.2001690">10.1145/2001576.2001690</a>.'
  mla: Mersmann, Olaf, et al. “Exploratory Landscape Analysis.” <i>Proceedings of
    the 13th Annual Conference on Genetic and Evolutionary Computation</i>, Association
    for Computing Machinery, 2011, pp. 829–836, doi:<a href="https://doi.org/10.1145/2001576.2001690">10.1145/2001576.2001690</a>.
  short: 'O. Mersmann, B. Bischl, H. Trautmann, M. Preuss, C. Weihs, G. Rudolph, in:
    Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation,
    Association for Computing Machinery, New York, NY, USA, 2011, pp. 829–836.'
date_created: 2023-08-04T15:58:22Z
date_updated: 2023-10-16T13:54:34Z
department:
- _id: '34'
- _id: '819'
doi: 10.1145/2001576.2001690
keyword:
- exploratory landscape analysis
- evolutionary optimization
- fitness landscape
- benchmarking
- BBOB test set
language:
- iso: eng
page: 829–836
place: New York, NY, USA
publication: Proceedings of the 13th Annual Conference on Genetic and Evolutionary
  Computation
publication_identifier:
  isbn:
  - '9781450305570'
publisher: Association for Computing Machinery
series_title: GECCO ’11
status: public
title: Exploratory Landscape Analysis
type: conference
user_id: '15504'
year: '2011'
...