97 citations · 333 across the 28 of their papers we have counts for
28 papers
How Good Is Neural Combinatorial Optimization? A Systematic Evaluation on the Traveling Salesman Problem
Shengcai Liu, Yu Zhang, Ke Tang +1
Traditional solvers for tackling combinatorial optimization (CO) problems are usually designed by human experts. Recently, there has been a surge of interest in utilizing deep lear…
Active Reinforcement Learning over MDPs
Qi Yang, Peng Yang, Ke Tang
The past decade has seen the rapid development of Reinforcement Learning, which acquires impressive performance with numerous training resources. However, one of the greatest chall…
Multi-objective Evolutionary Algorithms are Generally Good: Maximizing Monotone Submodular Functions over Sequences
Chao Qian, Dan-Xuan Liu, Chao Feng +1
Evolutionary algorithms (EAs) are general-purpose optimization algorithms, inspired by natural evolution. Recent theoretical studies have shown that EAs can achieve good approximat…
A Survey on Neural Network Interpretability
Yu Zhang, Peter Tiňo, Aleš Leonardis +1
Along with the great success of deep neural networks, there is also growing concern about their black-box nature. The interpretability issue affects people's trust on deep learning…
Memetic Search for Vehicle Routing with Simultaneous Pickup-Delivery and Time Windows
Shengcai Liu, Ke Tang, Xin Yao
The Vehicle Routing Problem with Simultaneous Pickup-Delivery and Time Windows (VRPSPDTW) has attracted much research interest in the last decade, due to its wide application in mo…
Evolutionary Reinforcement Learning via Cooperative Coevolutionary Negatively Correlated Search
Hu Zhang, Peng Yang, Yanglong Yu +2
Evolutionary algorithms (EAs) have been successfully applied to optimize the policies for Reinforcement Learning (RL) tasks due to their exploration ability. The recently proposed…