6 papers
A Decoupled Basis-Vector-Driven Generative Framework for Dynamic Multi-Objective Optimization
Yaoming Yang, Shuai Wang, Bingdong Li +2
Dynamic multi-objective optimization requires continuous tracking of moving Pareto fronts. Existing methods struggle with irregular mutations and data sparsity, primarily facing th…
Evolutionary Reinforcement Learning based AI tutor for Socratic Interdisciplinary Instruction
Mei Jiang, Haihai Shen, Zhuo Luo +4
Cultivating higher-order cognitive abilities -- such as knowledge integration, critical thinking, and creativity -- in modern STEM education necessitates a pedagogical shift from p…
Surrogate-Assisted Evolutionary Reinforcement Learning Based on Autoencoder and Hyperbolic Neural Network
Bingdong Li, Mei Jiang, Hong Qian +3
Evolutionary Reinforcement Learning (ERL), training the Reinforcement Learning (RL) policies with Evolutionary Algorithms (EAs), have demonstrated enhanced exploration capabilities…
Towards Calibrating Financial Market Simulators with High-frequency Data
Peng Yang, Junji Ren, Feng Wang +1
The fidelity of financial market simulation is restricted by the so-called "non-identifiability" difficulty when calibrating high-frequency data. This paper first analyzes the inhe…
Context-aware Diversity Enhancement for Neural Multi-Objective Combinatorial Optimization
Yongfan Lu, Zixiang Di, Bingdong Li +5
Multi-objective combinatorial optimization (MOCO) problems are prevalent in various real-world applications. Most existing neural MOCO methods rely on problem decomposition to tran…
It's Morphing Time: Unleashing the Potential of Multiple LLMs via Multi-objective Optimization
Bingdong Li, Zixiang Di, Yanting Yang +5
In this paper, we introduce a novel approach for addressing the multi-objective optimization problem in large language model merging via black-box multi-objective optimization algo…