14 citations · 18 across the 9 of their papers we have counts for
11 papers
Structure-Guided Masked Autoencoders for Ultra-High Resolution Scientific Image Understanding
Enzhi Zhang, Du Wu, Rui Zhong +18
Self-supervised pre-training with Vision Transformers, including Masked Autoencoders (MAE), is difficult to apply to gigapixel scientific images. Random masking is poorly matched t…
G-ICSO-NAS: Shifting Gears between Gradient and Swarm for Robust Neural Architecture Search
Xingbang Du, Enzhi Zhang, Rui Zhong +2
Neural Architecture Search (NAS) has become a pivotal technique in automated machine learning. Evolutionary Algorithm (EA)-based methods demonstrate superior search quality but suf…
Introducing Competitive Mechanism to Differential Evolution for Numerical Optimization
Rui Zhong, Yang Cao, Enzhi Zhang +1
This paper introduces a novel competitive mechanism into differential evolution (DE), presenting an effective DE variant named competitive DE (CDE). CDE features a simple yet effic…
Large Language Model Assisted Adversarial Robustness Neural Architecture Search
Rui Zhong, Yang Cao, Jun Yu +1
Motivated by the potential of large language models (LLMs) as optimizers for solving combinatorial optimization problems, this paper proposes a novel LLM-assisted optimizer (LLMO)…
Adaptive Patching for High-resolution Image Segmentation with Transformers
Enzhi Zhang, Isaac Lyngaas, Peng Chen +5
Attention-based models are proliferating in the space of image analytics, including segmentation. The standard method of feeding images to transformer encoders is to divide the ima…
Accelerating the Evolutionary Algorithms by Gaussian Process Regression with -greedy acquisition function
Rui Zhong, Enzhi Zhang, Masaharu Munetomo
In this paper, we propose a novel method to estimate the elite individual to accelerate the convergence of optimization. Inspired by the Bayesian Optimization Algorithm (BOA), the…
Accelerating the Genetic Algorithm for Large-scale Traveling Salesman Problems by Cooperative Coevolutionary Pointer Network with Reinforcement Learning
Rui Zhong, Enzhi Zhang, Masaharu Munetomo
In this paper, we propose a two-stage optimization strategy for solving the Large-scale Traveling Salesman Problems (LSTSPs) named CCPNRL-GA. First, we hypothesize that the partici…
Cooperative coevolutionary Modified Differential Evolution with Distance-based Selection for Large-Scale Optimization Problems in noisy environments through an automatic Random Grouping
Rui Zhong, Masaharu Munetomo
Many optimization problems suffer from noise, and nonlinearity check-based decomposition methods (e.g. Differential Grouping) will completely fail to detect the interactions betwee…
Accelerating differential evolution algorithm with Gaussian sampling based on estimating the convergence points
Rui Zhong, Masaharu Munetomo
In this paper, we propose a simple strategy for estimating the convergence point approximately by averaging the elite sub-population. Based on this idea, we derive two methods, whi…
Cooperative coevolutionary hybrid NSGA-II with Linkage Measurement Minimization for Large-scale Multi-objective optimization
Rui Zhong, Masaharu Munetomo
In this paper, we propose a variable grouping method based on cooperative coevolution for large-scale multi-objective problems (LSMOPs), named Linkage Measurement Minimization (LMM…
GTOPX Space Mission Benchmarks
Martin Schlueter, Mehdi Neshat, Mohamed Wahib +2
This contribution introduces the GTOPX space mission benchmark collection, which is an extension of GTOP database published by the European Space Agency (ESA). GTOPX consists of te…