This article proposes a novel differential evolution algorithm for solving constrained multimodal multiobjective optimization problems (CMMOPs), which may have multiple feasible Pareto-optimal solu…
The evolutionary algorithm recommendation is catching increasing attention when solving practical application problems since different algorithms often perform differently on different problems. To…
Feature selection is to reduce both the dimensionality of data and the classification error rate (i.e., increase the classification accuracy) of a learning algorithm. The two objectives are often c…
When solving constrained multiobjective optimization problems (CMOPs), the utilization of infeasible solutions significantly affects algorithm’s performance because they not only maintain diversi…
Handling constrained multiobjective optimization problems (CMOPs) is extremely challenging, since multiple conflicting objectives subject to various constraints require to be simultaneously optimiz…
In this work, we report on a high-power all-fiber ultrafast laser system at 2.0 μm that delivers femtosecond pulses with a fundamental repetition rate of 3.0 GHz and a maximum output power of 33.1…
Sparsity-promoting regularizers are widely used to impose low-complexity structure (e.g., l1-norm for sparsity) to the regression coefficients of supervised learning. In the realm of deterministic …
Monitoring, a digital surveillance technology that allows employers to track the activities of workers, is ubiquitous in the gig economy wherein the workforce is geographically dispersed. However, …
This article proposes an innovative method for constructing confidence intervals and assessing p-values in statistical inference for high-dimensional linear models. The proposed method has successf…
As a variant of the classical trust-region method for unconstrained optimization, the cubic regularization of the Newton method introduces a cubic regularization term in the surrogate objective to …