Exploratory Landscape Analysis is Strongly Sensitive to the Sampling Strategy
Parallel Problem Solving from Nature(2020)
摘要
Exploratory landscape analysis (ELA) supports supervised learning approaches for automated algorithm selection and configuration by providing sets of features that quantify the most relevant characteristics of the optimization problem at hand. In black-box optimization, where an explicit problem representation is not available, the feature values need to be approximated from a small number of sample points. In practice, uniformly sampled random point sets and Latin hypercube constructions are commonly used sampling strategies.
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关键词
Exploratory landscape analysis,Automated algorithm design,Black-box optimization,Feature extraction
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