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Detecting Funnel Structures by Means of Exploratory Landscape Analysis

Published: 11 July 2015 Publication History

Abstract

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 techniques exploiting the global structure of the function. The recently proposed Exploratory Landscape Analysis approach automatically identifies problem characteristics based on a moderately small initial sample of the objective function and proved to be effective for algorithm selection problems in continuous black-box optimization. In this paper, specific features for detecting funnel structures are introduced and combined with the existing ones in order to classify optimization problems regarding the funnel property. The effectiveness of the approach is shown by experiments on specifically generated test instances and validation experiments on standard benchmark problems.

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cover image ACM Conferences
GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation
July 2015
1496 pages
ISBN:9781450334723
DOI:10.1145/2739480
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Published: 11 July 2015

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Author Tags

  1. exploratory landscape analysis
  2. feature selection
  3. fitness landscapes
  4. funnel structure
  5. machine learning
  6. optimization
  7. working principles of evolutionary computing

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GECCO '15 Paper Acceptance Rate 182 of 505 submissions, 36%;
Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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  • (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
  • (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
  • (2024)Impact of Training Instance Selection on Automated Algorithm Selection Models for Numerical Black-box OptimizationProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654100(1007-1016)Online publication date: 14-Jul-2024
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