Evolutionary algorithms (EA) comprises population based algorithms that uses biologically inspired operators for optimization. DE and CMAES/IPOP are two powerful forms of EA that act on real numbers in order to provide solutions to multidimensional problems. Previously, researchers have tried to compare these two algorithms head-to-head, but no attempt
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Optimizing hip exoskeleton assistance pattern based on machine learning and simulation algorithms: a personalized approach to metabolic cost reduction. [PDF]
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RMETNet: A cross-subject motor imagery EEG signal classification model based on TSLANet and riemannian geometry features. [PDF]
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Chen B, Li J, Li H.
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A two phase differential evolution algorithm with perturbation and covariance matrix for PEMFC parameter estimation challenges. [PDF]
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Discriminating models of trait evolution. [PDF]
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Agile human activity recognition for wearable devices based on online incremental learning. [PDF]
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BDDN: bayesian dynamic differential network analysis in cancer proteomics. [PDF]
Kim J, Lee D, Park J, Jin IH, Ha MJ.
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Moving-Target Tracking in Airport Airside Operations Using AIMM-STUKF. [PDF]
Gao J, Dang Y, Zhu Y, Xue W.
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