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Exploratory landscape analysis

Authors:

Günter RudolphAuthors Info & Claims

GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation

Pages 829 - 836

https://doi.org/10.1145/2001576.2001690

Published: 12 July 2011 Publication History

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Abstract

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.

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Cited By

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Yang XWang RLi KIshibuchi H(2025)Meta-Black-Box optimization for evolutionary algorithms: Review and perspectiveSwarm and Evolutionary Computation10.1016/j.swevo.2024.10183893(101838)Online publication date: Mar-2025
https://doi.org/10.1016/j.swevo.2024.101838
Seiler MŠkvorc UDoerr CTrautmann H(2025)Synergies of Deep and Classical Exploratory Landscape Features for Automated Algorithm SelectionLearning and Intelligent Optimization10.1007/978-3-031-75623-8_29(361-376)Online publication date: 3-Jan-2025
https://doi.org/10.1007/978-3-031-75623-8_29
Xu SLiu WWu CLi J(2024)CNN-HT: A Two-Stage Algorithm Selection FrameworkEntropy10.3390/e2603026226:3(262)Online publication date: 14-Mar-2024
https://doi.org/10.3390/e26030262
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Index Terms

Exploratory landscape analysis
1. Mathematics of computing
  1. Mathematical analysis
    1. Mathematical optimization
  2. Probability and statistics
    1. Statistical paradigms
      1. Statistical graphics
2. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization

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Published In

GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation

July 2011

2140 pages

ISBN:9781450305570

DOI:10.1145/2001576

Editor:
Natalio Krasnogor
University of Nottingham, UK
,
General Chair:
Pier Luca Lanzi
Politecnico di Milano, Italy

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 12 July 2011

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SIGEVO

GECCO '11: Genetic and Evolutionary Computation Conference

July 12 - 16, 2011

Dublin, Ireland

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Yang XWang RLi KIshibuchi H(2025)Meta-Black-Box optimization for evolutionary algorithms: Review and perspectiveSwarm and Evolutionary Computation10.1016/j.swevo.2024.10183893(101838)Online publication date: Mar-2025
https://doi.org/10.1016/j.swevo.2024.101838
Seiler MŠkvorc UDoerr CTrautmann H(2025)Synergies of Deep and Classical Exploratory Landscape Features for Automated Algorithm SelectionLearning and Intelligent Optimization10.1007/978-3-031-75623-8_29(361-376)Online publication date: 3-Jan-2025
https://doi.org/10.1007/978-3-031-75623-8_29
Xu SLiu WWu CLi J(2024)CNN-HT: A Two-Stage Algorithm Selection FrameworkEntropy10.3390/e2603026226:3(262)Online publication date: 14-Mar-2024
https://doi.org/10.3390/e26030262
Prager RTrautmann H(2024)Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in PythonEvolutionary Computation10.1162/evco_a_00341(1-6)Online publication date: 15-Feb-2024
https://doi.org/10.1162/evco_a_00341
Long Fvan Stein BFrenzel MKrause PGitterle MBäck T(2024)Generating Cheap Representative Functions for Expensive Automotive Crashworthiness OptimizationACM Transactions on Evolutionary Learning and Optimization10.1145/36465544:2(1-26)Online publication date: 13-Feb-2024
https://dl.acm.org/doi/10.1145/3646554
Yoshikawa TTanabe RLi XHandl J(2024)On Constructing Algorithm Portfolios in Algorithm Selection for Computationally Expensive Black-box Optimization in the Fixed-budget SettingProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3664127(1754-1761)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638530.3664127
Yasuda YTamura KYasuda KLi XHandl J(2024)Analyzing Violation Landscapes Using Different Definitions of Constraint ViolationProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3664118(1815-1823)Online publication date: 14-Jul-2024
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Nikolikj AEftimov TLi XHandl J(2024)Comparing Solvability Patterns of Algorithms across Diverse Problem LandscapesProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3654305(143-146)Online publication date: 14-Jul-2024
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Korošec PEftimov TLi XHandl J(2024)Per-Run Algorithm Performance Improvement Forecasting Using Exploratory Landscape Analysis Features: A Case Study in Single-Objective Black-Box OptimizationProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3654304(571-574)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638530.3654304
Gallagher MMunoz MLi XHandl J(2024)Towards an Improved Understanding of Features for More Interpretable Landscape AnalysisProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3654301(135-138)Online publication date: 14-Jul-2024
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Detecting Funnel Structures by Means of Exploratory Landscape Analysis

Algorithm selection based on exploratory landscape analysis and cost-sensitive learning

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

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