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Yaochu Jin

8 papers hereh-index 576 citations30 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author5
  • last author3

Across the 8 of 8 papers where every author was matched, so the position is known.

fields
  • cs.NE4
  • cs.LG2
  • cs.AI1
  • math.OC1
same name
  • Yaochu Jin — 60 papers, h 115
  • Yaochu Jin — 14 papers, h 3
  • Yaochu Jin — 9 papers, h 4
  • Yaochu Jin — 8 papers, h 3
  • Yaochu Jin — 7 papers, h 14
  • Yaochu Jin — 6 papers, h 4

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20242026
most citedA Novel Dual-Stage Evolutionary Algorithm for Finding Robust Solutions

1 citations · 1 across the 8 of their papers we have counts for

collaborators
Showing cs.NEShow all

4 papers · 1 filter

cs.NE2026

An Evolutionary Algorithm Assisted by an Ensemble of Pareto-Optimal Surrogate Models

Kei Nishihara, Yaochu Jin, Masaya Nakata

An ensemble of surrogate models helps improve the prediction quality and robustness of surrogate models, and in turn, the search performance of surrogate-assisted evolutionary algo…

cs.NE2024

An Enhanced Differential Grouping Method for Large-Scale Overlapping Problems

Maojiang Tian, Mingke Chen, Wei Du +2

Large-scale overlapping problems are prevalent in practical engineering applications, and the optimization challenge is significantly amplified due to the existence of shared varia…

cs.NE2024

Knowledge-Assisted Dual-Stage Evolutionary Optimization of Large-Scale Crude Oil Scheduling

Wanting Zhang, Wei Du, Guo Yu +3

With the scaling up of crude oil scheduling in modern refineries, large-scale crude oil scheduling problems (LSCOSPs) emerge with thousands of binary variables and non-linear const…

cs.NE2024★ 1 cited

A Novel Dual-Stage Evolutionary Algorithm for Finding Robust Solutions

Wei Du, Wenxuan Fang, Chen Liang +2

In robust optimization problems, the magnitude of perturbations is relatively small. Consequently, solutions within certain regions are less likely to represent the robust optima w…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.