8 citations · 8 across the 8 of their papers we have counts for
9 papers
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…
Amortized Multi-Objective Optimization Across Tasks with Generative Solution Modeling
Tingyang Wei, Jiao Liu, Abhishek Gupta +3
Many real-world applications require solving families of expensive multi-objective optimization problems~(EMOPs) under varying operational conditions. This can be formulated as par…
MazeMate: An LLM-Powered Chatbot to Support Computational Thinking in Gamified Programming Learning
Chenyu Hou, Hua Yu, Gaoxia Zhu +3
Computational Thinking (CT) is a foundational problem-solving skill, and gamified programming environments are a widely adopted approach to cultivating it. While large language mod…
EvoSpeak: Large Language Models for Interpretable Genetic Programming-Evolved Heuristics
Meng Xu, Jiao Liu, Yew Soon Ong
Genetic programming (GP) has demonstrated strong effectiveness in evolving tree-structured heuristics for complex optimization problems. Yet, in dynamic and large-scale scenarios,…
Multi-Objective Recommendation in the Era of Generative AI: A Survey of Recent Progress and Future Prospects
Zihan Hong, Yushi Wu, Zhiting Zhao +4
With the recent progress in generative artificial intelligence (Generative AI), particularly in the development of large language models, recommendation systems are evolving to bec…
()-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…