9 citations · 16 across the 12 of their papers we have counts for
12 papers
AutoPSO: A Meta-framework for Automated Particle Swarm Optimization
Xinmeng Yu, Jiaxin Gao, Jianguo Zhang +2
Particle swarm optimization (PSO) is a widely used metaheuristic, prized for its simplicity and small parameter set. Although decades of research have produced numerous PSO variant…
EvoGM: Learning to Merge LLMs via Evolutionary Generative Optimization
Tao Jiang, Xinmeng Yu, Chenhao Yi +5
Evolutionary model merging provides a powerful framework for the automated, training-free composition of LLMs through parameter-space search. However, existing methods predominantl…
A Multi-objective Evolutionary Algorithm Based on Bi-population with Uniform Sampling for Neural Architecture Search
Yu Xue, Pengcheng Jiang, Chenchen Zhu +4
Neural architecture search (NAS) automates neural network design, improving efficiency over manual approaches. However, efficiently discovering high-performance neural network arch…
Unleashing the Potential of Differential Evolution through Individual-Level Strategy Diversity
Chenchen Feng, Minyang Chen, Zhuozhao Li +1
Since Differential Evolution (DE) is sensitive to strategy choice, most existing variants pursue performance through adaptive mechanisms or intricate designs. While these approache…
TensorNEAT: A GPU-accelerated Library for NeuroEvolution of Augmenting Topologies
Lishuang Wang, Mengfei Zhao, Enyu Liu +2
The NeuroEvolution of Augmenting Topologies (NEAT) algorithm has received considerable recognition in the field of neuroevolution. Its effectiveness is derived from initiating with…
GPU-accelerated Evolutionary Many-objective Optimization Using Tensorized NSGA-III
Hao Li, Zhenyu Liang, Ran Cheng
NSGA-III is one of the most widely adopted algorithms for tackling many-objective optimization problems. However, its CPU-based design severely limits scalability and computational…
Bridging Evolutionary Multiobjective Optimization and GPU Acceleration via Tensorization
Zhenyu Liang, Hao Li, Naiwei Yu +2
Evolutionary multiobjective optimization (EMO) has made significant strides over the past two decades. However, as problem scales and complexities increase, traditional EMO algorit…
Enabling Population-Level Parallelism in Tree-Based Genetic Programming for GPU Acceleration
Zhihong Wu, Lishuang Wang, Kebin Sun +2
Tree-based Genetic Programming (TGP) is a widely used evolutionary algorithm for tasks such as symbolic regression, classification, and robotic control. Due to the intensive comput…
A Multi-objective Optimization Benchmark Test Suite for Real-time Semantic Segmentation
Yifan Zhao, Zhenyu Liang, Zhichao Lu +1
As one of the emerging challenges in Automated Machine Learning, the Hardware-aware Neural Architecture Search (HW-NAS) tasks can be treated as black-box multi-objective optimizati…
Tensorized Ant Colony Optimization for GPU Acceleration
Luming Yang, Tao Jiang, Ran Cheng
Ant Colony Optimization (ACO) is renowned for its effectiveness in solving Traveling Salesman Problems, yet it faces computational challenges in CPU-based environments, particularl…
Tensorized NeuroEvolution of Augmenting Topologies for GPU Acceleration
Lishuang Wang, Mengfei Zhao, Enyu Liu +2
The NeuroEvolution of Augmenting Topologies (NEAT) algorithm has received considerable recognition in the field of neuroevolution. Its effectiveness is derived from initiating with…
GPU-accelerated Evolutionary Multiobjective Optimization Using Tensorized RVEA
Zhenyu Liang, Tao Jiang, Kebin Sun +1
Evolutionary multiobjective optimization has witnessed remarkable progress during the past decades. However, existing algorithms often encounter computational challenges in large-s…