9 papers
MeEvo: Metacognitive Evolution Combined with Natural Evolution for Automatic Heuristic Design
Zishang Qiu, Xinan Chen, Rong Qu +1
Large Language Models (LLMs) have advanced Automatic Heuristic Design (AHD) by enabling heuristic generation through reasoning and code synthesis. In LLM-based AHD, the LLM reasons…
MotionMERGE: A Multi-granular Framework for Human Motion Editing, Reasoning, Generation, and Explanation
Bizhu Wu, Jinheng Xie, Wenting Chen +5
Recent motion-language models unify tasks like comprehension and generation but operate at a coarse granularity, lacking fine-grained understanding and nuanced control over body pa…
Preference-Agile Multi-Objective Optimization for Real-time Vehicle Dispatching
Jiahuan Jin, Wenhao Zhao, Rong Qu +4
Multi-objective optimization (MOO) has been widely studied in literature because of its versatility in human-centered decision making in real-life applications. Recently, demand fo…
ReactMotion: Generating Reactive Listener Motions from Speaker Utterance
Cheng Luo, Bizhu Wu, Bing Li +5
In this paper, we introduce a new task, Reactive Listener Motion Generation from Speaker Utterance, which aims to generate naturalistic listener body motions that appropriately res…
MeLA: A Metacognitive LLM-Driven Architecture for Automatic Heuristic Design
Zishang Qiu, Xinan Chen, Long Chen +1
This paper introduces MeLA, a Metacognitive LLM-Driven Architecture that presents a new paradigm for Automatic Heuristic Design (AHD). Traditional evolutionary methods operate dire…
Genetic Programming with Reinforcement Learning Trained Transformer for Real-World Dynamic Scheduling Problems
Xinan Chen, Rong Qu, Jing Dong +2
Dynamic scheduling in real-world environments often struggles to adapt to unforeseen disruptions, making traditional static scheduling methods and human-designed heuristics inadequ…