6 papers
Evolutionary Intelligence for Scientific Discovery: From Evolutionary Computation to Cumulative Discovery Systems
Chao Wang, Lingling Li, Fang Liu +1
Artificial intelligence (AI) is shifting scientific discovery from task-specific workflows towards autonomous systems that organize exploration with experimental and human feedback…
Model Merging to Evolution: Parameter Space Exploration for Expert Models
Chao Wang, Yuchen Guo, Zheng Tan +4
Model merging integrates the capabilities of multiple expert models to create strong models for multiple tasks without additional training, thereby reducing computational resource…
Learning Evolution via Optimization Knowledge Adaptation
Chao Wang, Lingling Li, Licheng Jiao +3
The iterative search process of evolutionary algorithms (EAs) encapsulates optimization knowledge within historical populations and fitness evaluations. Effective utilization of th…
Task-free Adaptive Meta Black-box Optimization
Chao Wang, Licheng Jiao, Lingling Li +4
Handcrafted optimizers become prohibitively inefficient for complex black-box optimization (BBO) tasks. MetaBBO addresses this challenge by meta-learning to automatically configure…
When Large Language Models Meet Evolutionary Algorithms: Potential Enhancements and Challenges
Chao Wang, Jiaxuan Zhao, Licheng Jiao +3
Pre-trained large language models (LLMs) exhibit powerful capabilities for generating natural text. Evolutionary algorithms (EAs) can discover diverse solutions to complex real-wor…
Knowledge-aware Evolutionary Graph Neural Architecture Search
Chao Wang, Jiaxuan Zhao, Lingling Li +4
Graph neural architecture search (GNAS) can customize high-performance graph neural network architectures for specific graph tasks or datasets. However, existing GNAS methods begin…