5 citations · 13 across the 7 of their papers we have counts for
7 papers
ParetoLens: A Visual Analytics Framework for Exploring Solution Sets of Multi-objective Evolutionary Algorithms
Yuxin Ma, Zherui Zhang, Ran Cheng +2
In the domain of multi-objective optimization, evolutionary algorithms are distinguished by their capability to generate a diverse population of solutions that navigate the trade-o…
Multidomain Evolutionary Optimization on Combinatorial Problems in Complex Networks
Jie Zhao, Kang Hao Cheong, Yaochu Jin
Knowledge transfer-based evolutionary optimization has garnered significant attention, such as in multi-task evolutionary optimization (MTEO), which aims to solve complex problems…
EmoDM: A Diffusion Model for Evolutionary Multi-objective Optimization
Xueming Yan, Yaochu Jin
Evolutionary algorithms have been successful in solving multi-objective optimization problems (MOPs). However, as a class of population-based search methodology, evolutionary algor…
Multiform Evolution for High-Dimensional Problems with Low Effective Dimensionality
Yaqing Hou, Mingyang Sun, Abhishek Gupta +4
In this paper, we scale evolutionary algorithms to high-dimensional optimization problems that deceptively possess a low effective dimensionality (certain dimensions do not signifi…
HE-DKSAP: Privacy-Preserving Stealth Address Protocol via Additively Homomorphic Encryption
Yuping Yan, George Shao, Dennis Song +2
Blockchain transactions have gained widespread adoption across various industries, largely attributable to their unparalleled transparency and robust security features. Nevertheles…
An Edge-Aware Graph Autoencoder Trained on Scale-Imbalanced Data for Traveling Salesman Problems
Shiqing Liu, Xueming Yan, Yaochu Jin
In recent years, there has been a notable surge in research on machine learning techniques for combinatorial optimization. It has been shown that learning-based methods outperform…
Pareto-wise Ranking Classifier for Multi-objective Evolutionary Neural Architecture Search
Lianbo Ma, Nan Li, Guo Yu +3
In the deployment of deep neural models, how to effectively and automatically find feasible deep models under diverse design objectives is fundamental. Most existing neural archite…