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A New EDA with Dimension Reduction Technique for Large Scale Many-Objective Optimization
The performance of many-objective evolutionary algorithms deteriorates appreciably in solving large-scale many-objective optimization problems (MaOPs) which encompass more than hundreds variables. One of the known rationales is the curse of dimensionality. Estimation of distribution algorithms sampl...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
2020
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7354802/ http://dx.doi.org/10.1007/978-3-030-53956-6_33 |