151 citations · 606 across the 63 of their papers we have counts for
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ASBSO: An Improved Brain Storm Optimization With Flexible Search Length and Memory-Based Selection
Yang Yu, Shangce Gao, Yirui Wang +2
Brain storm optimization (BSO) is a newly proposed population-based optimization algorithm, which uses a logarithmic sigmoid transfer function to adjust its search range during the…
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…
Analysis of Noisy Evolutionary Optimization When Sampling Fails
Chao Qian, Chao Bian, Yang Yu +2
In noisy evolutionary optimization, sampling is a common strategy to deal with noise. By the sampling strategy, the fitness of a solution is evaluated multiple times (called \emph{…
A Lower Bound Analysis of Population-based Evolutionary Algorithms for Pseudo-Boolean Functions
Chao Qian, Yang Yu, Zhi-Hua Zhou
Evolutionary algorithms (EAs) are population-based general-purpose optimization algorithms, and have been successfully applied in various real-world optimization tasks. However, pr…
Towards Analyzing Crossover Operators in Evolutionary Search via General Markov Chain Switching Theorem
Yang Yu, Chao Qian, Zhi-Hua Zhou
Evolutionary algorithms (EAs), simulating the evolution process of natural species, are used to solve optimization problems. Crossover (also called recombination), originated from…
On the approximation ability of evolutionary optimization with application to minimum set cover
Yang Yu, Xin Yao, Zhi-Hua Zhou
Evolutionary algorithms (EAs) are heuristic algorithms inspired by natural evolution. They are often used to obtain satisficing solutions in practice. In this paper, we investigate…