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High-dimensional normalized data profiles for testing derivative-free optimization algorithms
This article provides a new tool for examining the efficiency and robustness of derivative-free optimization algorithms based on high-dimensional normalized data profiles that test a variety of performance metrics. Unlike the traditional data profiles that examine a single dimension, the proposed da...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
PeerJ Inc.
2022
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9454945/ https://www.ncbi.nlm.nih.gov/pubmed/36091977 http://dx.doi.org/10.7717/peerj-cs.960 |