How Far Out of Distribution Can We Go With ELA Features and Still Be Able to Rank Algorithms?
Author:
Affiliation:
1. Jožef Stefan Institute, Jožef Stefan International, Postgraduate School,Computer Systems Department,Ljubljana,Slovenia
Funder
Slovenian Research Agency
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10371778/10371788/10371880.pdf?arnumber=10371880
Reference27 articles.
1. Evolutionary algorithms and their applications to engineering problems
2. The Algorithm Selection Problem
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4. Exploratory landscape analysis
5. TLA: Topological Landscape Analysis for Single-Objective Continuous Optimization Problem Instances
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1. TransOptAS: Transformer-Based Algorithm Selection for Single-Objective Optimization;Proceedings of the Genetic and Evolutionary Computation Conference Companion;2024-07-14
2. On Generalization of ELA Feature Groups;Proceedings of the Genetic and Evolutionary Computation Conference Companion;2024-07-14
3. Impact of Scaling in ELA Feature Calculation on Algorithm Selection Cross-Benchmark Transferability;2024 IEEE Congress on Evolutionary Computation (CEC);2024-06-30
4. Learned Features vs. Classical ELA on Affine BBOB Functions;Lecture Notes in Computer Science;2024
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