2 papers
cs.NE2026
Decoupling Numerical and Structural Parameters: An Empirical Study on Adaptive Genetic Algorithms via Deep Reinforcement Learning for the Large-Scale TSP
Hongyu Wang, Yuhan Jing, Yibing Shi +3
Proper parameter configuration is a prerequisite for the success of Evolutionary Algorithms (EAs). While various adaptive strategies have been proposed, it remains an open question…
cs.LG2026
Adaptive Context Length Optimization with Low-Frequency Truncation for Multi-Agent Reinforcement Learning
Wenchang Duan, Yaoliang Yu, Jiwan He +1
Recently, deep multi-agent reinforcement learning (MARL) has demonstrated promising performance for solving challenging tasks, such as long-term dependencies and non-Markovian envi…