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- W2017258920 abstract "Multimodal optimization is an important area of active research within the evolutionary computation community. The ability of algorithms to discover and maintain multiple optima is of great importance - in particular when several global optima exist or when other high-quality solutions might be of interest. The differential evolution algorithm (DE) is extended with a crowding scheme making it capable of tracking and maintaining multiple optima. The introduced CrowdingDE algorithm is compared with a DE using the well-known sharing scheme that penalizes similar candidate solutions. In conclusion, the introduced CrowdingDE outperformed the sharing-based DE algorithm on fourteen commonly used benchmark problems." @default.
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- W2017258920 date "2005-01-17" @default.
- W2017258920 modified "2023-10-18" @default.
- W2017258920 title "Multimodal optimization using crowding-based differential evolution" @default.
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- W2017258920 doi "https://doi.org/10.1109/cec.2004.1331058" @default.
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