9 citations · 19 across the 7 of their papers we have counts for
5 papers · 1 filter
Semantics-Guided Automatic Tensorization for Multiobjective Evolutionary Algorithms: A Multi-Agent Framework
Zhenyu Liang, Beichen Huang, Bowen Zheng +1
Multiobjective evolutionary algorithms (MOEAs) naturally expose population-level parallelism, but many mature implementations encode their computation in sequential program structu…
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…
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…
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…