4 papers
Evolutionary Generative Optimization: Towards Fully Data-Driven Evolutionary Optimization via Generative Learning
Tao Jiang, Kebin Sun, Zhenyu Liang +3
Recent advances in data-driven evolutionary algorithms (EAs) have demonstrated the potential of leveraging historical data to improve optimization accuracy and adaptability. Despit…
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…
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…
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…