97 citations · 297 across the 11 of their papers we have counts for
11 papers
Evolving Generalizable Parallel Algorithm Portfolios for Binary Optimization Problems via Domain-Agnostic Instance Generation
Zhiyuan Wang, Shengcai Liu, Peng Yang +1
Generalization is the core objective when training optimizers from data. However, limited training instances often constrain the generalization capability of the trained optimizers…
Reducing Idleness in Financial Cloud Services via Multi-objective Evolutionary Reinforcement Learning based Load Balancer
Peng Yang, Laoming Zhang, Haifeng Liu +1
In recent years, various companies have started to shift their data services from traditional data centers to the cloud. One of the major motivations is to save on operational cost…
Enabling surrogate-assisted evolutionary reinforcement learning via policy embedding
Lan Tang, Xiaxi Li, Jinyuan Zhang +3
Evolutionary Reinforcement Learning (ERL) that applying Evolutionary Algorithms (EAs) to optimize the weight parameters of Deep Neural Network (DNN) based policies has been widely…
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…
Robust Dynamic Network Embedding via Ensembles
Chengbin Hou, Guoji Fu, Peng Yang +3
Dynamic Network Embedding (DNE) has recently attracted considerable attention due to the advantage of network embedding in various fields and the dynamic nature of many real-world…
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…
Few-shots Parallel Algorithm Portfolio Construction via Co-evolution
Ke Tang, Shengcai Liu, Peng Yang +1
Generalization, i.e., the ability of solving problem instances that are not available during the system design and development phase, is a critical goal for intelligent systems. A…
Parallel Exploration via Negatively Correlated Search
Peng Yang, Qi Yang, Ke Tang +1
Effective exploration is a key to successful search. The recently proposed Negatively Correlated Search (NCS) tries to achieve this by parallel exploration, where a set of search p…
A Parallel Divide-and-Conquer based Evolutionary Algorithm for Large-scale Optimization
Peng Yang, Ke Tang, Xin Yao
Large-scale optimization problems that involve thousands of decision variables have extensively arisen from various industrial areas. As a powerful optimization tool for many real-…
High-dimensional Black-box Optimization via Divide and Approximate Conquer
Peng Yang, Ke Tang, Xin Yao
Divide and Conquer (DC) is conceptually well suited to high-dimensional optimization by decomposing a problem into multiple small-scale sub-problems. However, appealing performance…
Negatively Correlated Search
Ke Tang, Peng Yang, Xin Yao
Evolutionary Algorithms (EAs) have been shown to be powerful tools for complex optimization problems, which are ubiquitous in both communication and big data analytics. This paper…