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Researcher profile

Chen Hao

Chen Hao contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Artificial IntelligenceMethodologyNeural and Evolutionary Computing

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Published work

2 published item(s)

preprint2026arXiv

RDEx-CASK: Cauchy Mutation, Archive, and Stagnation Kick for RDEx-CSOP

We extend RDEx-CSOP with 3 changes that target stagnation & late-stage variance, plus minor parameter tuning. The second scale factor in the standard branch is sampled independently from a truncated Cauchy. A small feasible-only JADE-style archive (|A|_max = 50) is added & sampled with probability |A|/(|A|+|P|). Per-individual stagnation counter triggers, after 180 no-improvement generations, three local overrides on standard branch: pull toward the global best, lift the archive sampling floor to 0.65, & saturate CR to 0.95 when population success rate is below 0.10. The exploitation biased branch & every other RDEx component are left untouched. On CEC CSOP suite (D=30, 25 runs), RDEx-CASK is competitive with RDEx, UDE-III, & CL-SRDE in feasibility-aware quality & improves time-to-target on most problems.

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preprint2020arXiv

Simultaneous Differential Network Analysis and Classification for High-dimensional Matrix-variate Data, with application to Brain Connectivity Alteration Detection and fMRI-guided Medical Diagnoses of Alzheimer's Disease

Alzheimer's disease (AD) is the most common form of dementia, which causes problems with memory, thinking and behavior. Growing evidence has shown that the brain connectivity network experiences alterations for such a complex disease. Network comparison, also known as differential network analysis, is thus particularly powerful to reveal the disease pathologies and identify clinical biomarkers for medical diagnoses (classification). Data from neurophysiological measurements are multi-dimensional and in matrix-form, which poses major challenges in brain connectivity analysis and medical diagnoses. Naive vectorization method is not sufficient as it ignores the structural information within the matrix. In the article, we adopt the Kronecker product covariance matrix framework to capture both spatial and temporal correlations of the matrix-variate data while the temporal covariance matrix is treated as a nuisance parameter. By recognizing that the strengths of network connections may vary across subjects, we develop an ensemble-learning procedure, which identifies the differential interaction patterns of brain regions between the AD group and the control group and conducts medical diagnosis (classification) of AD simultaneously. We applied the proposed procedure to functional connectivity analysis of fMRI dataset related with Alzheimer's disease. The hub nodes and differential interaction patterns identified are consistent with existing experimental studies, and satisfactory out-of-sample classification performance is achieved for medical diagnosis of Alzheimer's disease. An R package \SDNCMV" for implementation is available at https://github.com/heyongstat/SDNCMV.

0 citations0 reviewsNo code linkedOpen access