8 citations · 8 across the 8 of their papers we have counts for
5 papers · 1 filter
Novelty Search with Cross-Task Collaborative Discovery
Jiao Liu, Yanchi Li, Hua Yu +2
Novelty Search (NS) promotes exploration by rewarding behaviorally novel solutions rather than directly optimizing predefined objectives, making it particularly useful when objecti…
TransGP: Task-Conditioned Transformer-Guided Genetic Programming for Multitask Dynamic Flexible Job Shop Scheduling
Meng Xu, Jiao Liu, Hua Yu +1
Hyper-heuristics have become a popular approach for solving dynamic flexible job shop scheduling (DFJSS) problems. They use gradient-free optimization techniques like Genetic Progr…
()-Parametric Multi-Task Optimization: Joint Search in Solution and Infinite Task Spaces
Tingyang Wei, Jiao Liu, Abhishek Gupta +2
Multi-task optimization is typically characterized by a fixed and finite set of tasks. The present paper relaxes this condition by considering a non-fixed and potentially infinite…
Evolutionary Optimization of Physics-Informed Neural Networks: Evo-PINN Frontiers and Opportunities
Jian Cheng Wong, Abhishek Gupta, Chin Chun Ooi +3
Deep learning models trained on finite data lack a complete understanding of the physical world. On the other hand, physics-informed neural networks (PINNs) are infused with such k…
Language Model Evolutionary Algorithms for Recommender Systems: Benchmarks and Algorithm Comparisons
Jiao Liu, Zhu Sun, Shanshan Feng +2
In the evolutionary computing community, the remarkable language-handling capabilities and reasoning power of large language models (LLMs) have significantly enhanced the functiona…