35 citations · 41 across the 7 of their papers we have counts for
8 papers
Coarse-to-Fine Macro Placement via Evolutionary Search and Critical Macro Tuning
Biao Liu, Zhiping Jin, Kaixuan Sun +3
Macro placement is a critical stage in chip physical design that substantially affects downstream implementation quality. Recent search-based methods improve existing layouts throu…
Expanding Horizons of Level Diversity via Multi-objective Evolutionary Learning
Qingquan Zhang, Ziqi Wang, Yuchen Li +3
In recent years, the generation of diverse game levels has gained increasing interest, contributing to a richer and more engaging gaming experience. A number of level diversity met…
Ethical Considerations of Large Language Models in Game Playing
Qingquan Zhang, Yuchen Li, Bo Yuan +3
Large language models (LLMs) have demonstrated tremendous potential in game playing, while little attention has been paid to their ethical implications in those contexts. This work…
Exploring Accuracy-Fairness Trade-off in Large Language Models
Qingquan Zhang, Qiqi Duan, Bo Yuan +2
Large Language Models (LLMs) have made significant strides in the field of artificial intelligence, showcasing their ability to interact with humans and influence human cognition t…
Fairness-aware Multiobjective Evolutionary Learning
Qingquan Zhang, Jialin Liu, Xin Yao
Multiobjective evolutionary learning (MOEL) has demonstrated its advantages of training fairer machine learning models considering a predefined set of conflicting objectives, inclu…
Interpreting Multi-objective Evolutionary Algorithms via Sokoban Level Generation
Qingquan Zhang, Yuchen Li, Yuhang Lin +2
This paper presents an interactive platform to interpret multi-objective evolutionary algorithms. Sokoban level generation is selected as a showcase for its widespread use in proce…
Measuring Diversity of Game Scenarios
Yuchen Li, Ziqi Wang, Qingquan Zhang +2
This survey comprehensively reviews the multi-dimensionality of game scenario diversity, spotlighting the innovative use of procedural content generation and other fields as corner…
Mitigating Unfairness via Evolutionary Multi-objective Ensemble Learning
Qingquan Zhang, Jialin Liu, Zeqi Zhang +3
In the literature of mitigating unfairness in machine learning, many fairness measures are designed to evaluate predictions of learning models and also utilised to guide the traini…