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Exploratory Landscape Analysis (ELA) measures have been shown to predict algorithm performance; hence, they are being applied on critical tasks such as automatic algorithm selection and problem generation. This paper provides a cautionary examination on their use in black-box continuous optimization. We explore the effect that translations have on the measures, when the cost function is defined within...
This paper proposes a new methodology to determine the relative performance of optimization algorithms across various classes of instances. Rather than reporting performance based on a chosen test set of benchmark instances, we aim to develop metrics for an algorithm's performance generalized across a diverse set of instances. Instances are summarized by a set of features that correlate with difficulty,...
The purpose of instance selection is to identify which instances (examples, patterns) in a large dataset should be selected as representatives of the entire dataset, without significant loss of information. When a machine learning method is applied to the reduced dataset, the accuracy of the model should not be significantly worse than if the same method were applied to the entire dataset. The reducibility...
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