poster

Investigating the Viability of Existing Exploratory Landscape Analysis Features for Mixed-Integer Problems

Authors:

Raphael Patrick Prager,

Heike TrautmannAuthors Info & Claims

GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation

Pages 451 - 454

https://doi.org/10.1145/3583133.3590757

Published: 24 July 2023 Publication History

Get Access

Abstract

Exploratory landscape analysis has been at the forefront of characterizing single-objective continuous optimization problems. Other variants, which can be summarized under the term landscape analysis, have been used in the domain of combinatorial problems. However, none to little has been done in this research area for mixed-integer problems. In this work, we evaluate the current state of existing exploratory landscape analysis features and their applicability on a subset of mixed-integer problems.

References

[1]

Pascal Kerschke, Holger H. Hoos, Frank Neumann, and Heike Trautmann. 2019. Automated Algorithm Selection: Survey and Perspectives. Evolutionary Computation 27, 1 (2019), 3 -- 45.

Digital Library

Google Scholar

[2]

Pascal Kerschke and Heike Trautmann. 2019. Automated Algorithm Selection on Continuous Black-Box Problems By Combining Exploratory Landscape Analysis and Machine Learning. Evolutionary Computation 27, 1 (2019), 99 -- 127.

Digital Library

Google Scholar

[3]

Fu Xing Long, Bas van Stein, Moritz Frenzel, Peter Krause, Markus Gitterle, and Thomas Bäck. 2022. Learning the Characteristics of Engineering Optimization Problems with Applications in Automotive Crash (GECCO '22). Association for Computing Machinery, New York, NY, USA.

Digital Library

Google Scholar

[4]

Olaf Mersmann, Bernd Bischl, Heike Trautmann, Mike Preuss, Claus Weihs, and Günter Rudolph. 2011. Exploratory Landscape Analysis. In Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation (Dublin, Ireland) (GECCO '11). Association for Computing Machinery, New York, NY, USA, 829--836.

Digital Library

Google Scholar

[5]

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 2011. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12 (2011), 2825--2830.

Digital Library

Google Scholar

[6]

Raphael Patrick Prager and Heike Trautmann. 2023. Nullifying the Inherent Bias of Non-invariant Exploratory Landscape Analysis Features. In Applications of Evolutionary Computation, João Correia, Stephen Smith, and Raneem Qaddoura (Eds.). Springer Nature Switzerland, Cham, 411--425.

Google Scholar

[7]

Sebastian Raschka. 2018. MLxtend: Providing Machine Learning and Data Science Utilities and Extensions to Python's Scientific Computing Stack. The Journal of Open Source Software (JOSS) 3, 24 (April 2018).

Crossref

Google Scholar

[8]

Quentin Renau, Johann Dreo, Carola Doerr, and Benjamin Doerr. 2021. Towards Explainable Exploratory Landscape Analysis: Extreme Feature Selection for Classifying BBOB Functions. Proceedings of the 24th International Conference, EvoApplications 2021 12694 LNCS (2021), 17--33.

Digital Library

Google Scholar

[9]

Moritz Vinzent Seiler, Raphael Patrick Prager, Pascal Kerschke, and Heike Trautmann. 2022. A Collection of Deep Learning-Based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes. In Proceedings of the Genetic and Evolutionary Computation Conference (Boston, Massachusetts) (GECCO '22). Association for Computing Machinery, New York, NY, USA, 657--665.

Digital Library

Google Scholar

[10]

Tea Tušar, Dimo Brockhoff, and Nikolaus Hansen. 2019. Mixed-Integer Benchmark Problems for Single- and Bi-Objective Optimization. In Proceedings of the Genetic and Evolutionary Computation Conference (Prague, Czech Republic) (GECCO '19). Association for Computing Machinery, New York, NY, USA, 718--726.

Digital Library

Google Scholar

Cited By

View all

Dietrich KPrager RDoerr CTrautmann H(2024)Hybridizing Target- and SHAP-Encoded Features for Algorithm Selection in Mixed-Variable Black-Box OptimizationParallel Problem Solving from Nature – PPSN XVIII10.1007/978-3-031-70068-2_10(154-169)Online publication date: 14-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-70068-2_10

Index Terms

Investigating the Viability of Existing Exploratory Landscape Analysis Features for Mixed-Integer Problems
1. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization
      1. Mixed discrete-continuous optimization

Recommendations

Detecting Funnel Structures by Means of Exploratory Landscape Analysis
GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

In single-objective optimization different optimization strategies exist depending on the structure and characteristics of the underlying problem. In particular, the presence of so-called funnels in multimodal problems offers the possibility of applying ...
Exploratory landscape analysis
GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation

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. ...
An exploratory landscape analysis driven artificial bee colony algorithm with maximum entropic epistasis
Abstract
Continuous real-valued optimization problems are existed in various engineering optimization scenarios. The artificial bee colony algorithm (ABC) which is inspired by the foraging behavior of bee colonies is an efficient optimization ...
Highlights
- The MEEABC based on exploratory landscape analysis is proposed.
- The maximum ...

Comments

Information & Contributors

Information

Published In

GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation

July 2023

2519 pages

ISBN:9798400701207

DOI:10.1145/3583133

Chair:
Sara Silva,
Program Chair:
Luís Paquete

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 July 2023

Check for updates

Author Tags

Qualifiers

Poster

Conference

GECCO '23 Companion

Sponsor:

SIGEVO

GECCO '23 Companion: Companion Conference on Genetic and Evolutionary Computation

July 15 - 19, 2023

Lisbon, Portugal

Acceptance Rates

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
38
Total Downloads

Downloads (Last 12 months)14
Downloads (Last 6 weeks)1

Reflects downloads up to 27 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Dietrich KPrager RDoerr CTrautmann H(2024)Hybridizing Target- and SHAP-Encoded Features for Algorithm Selection in Mixed-Variable Black-Box OptimizationParallel Problem Solving from Nature – PPSN XVIII10.1007/978-3-031-70068-2_10(154-169)Online publication date: 14-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-70068-2_10

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Index Terms

Recommendations

Detecting Funnel Structures by Means of Exploratory Landscape Analysis

Exploratory landscape analysis

An exploratory landscape analysis driven artificial bee colony algorithm with maximum entropic epistasis

Comments

Information

Published In

Sponsors

Publisher

Publication History

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations