22 citations · 76 across the 26 of their papers we have counts for
15 papers · 1 filter
Stochastic Population Update Can Provably Be Helpful in Multi-Objective Evolutionary Algorithms
Chao Bian, Yawen Zhou, Miqing Li +1
Evolutionary algorithms (EAs) have been widely and successfully applied to solve multi-objective optimization problems, due to their nature of population-based search. Population u…
Can Evolutionary Clustering Have Theoretical Guarantees?
Chao Qian
Clustering is a fundamental problem in many areas, which aims to partition a given data set into groups based on some distance measure, such that the data points in the same group…
Analyzing the Expected Hitting Time of Evolutionary Computation-based Neural Architecture Search Algorithms
Zeqiong Lv, Chao Qian, Gary G. Yen +1
Evolutionary computation-based neural architecture search (ENAS) is a popular technique for automating architecture design of deep neural networks. Despite its groundbreaking appli…
Heterogeneous Multi-agent Zero-Shot Coordination by Coevolution
Ke Xue, Yutong Wang, Cong Guan +5
Generating agents that can achieve zero-shot coordination (ZSC) with unseen partners is a new challenge in cooperative multi-agent reinforcement learning (MARL). Recently, some stu…
Robust Subset Selection by Greedy and Evolutionary Pareto Optimization
Chao Bian, Yawen Zhou, Chao Qian
Subset selection, which aims to select a subset from a ground set to maximize some objective function, arises in various applications such as influence maximization and sensor plac…
Running Time Analysis of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) using Binary or Stochastic Tournament Selection
Chao Bian, Chao Qian
Evolutionary algorithms (EAs) have been widely used to solve multi-objective optimization problems, and have become the most popular tool. However, the theoretical foundation of mu…
Result Diversification by Multi-objective Evolutionary Algorithms with Theoretical Guarantees
Chao Qian, Dan-Xuan Liu, Zhi-Hua Zhou
Given a ground set of items, the result diversification problem aims to select a subset with high "quality" and "diversity" while satisfying some constraints. It arises in various…
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…
Multi-objective Evolutionary Algorithms are Still Good: Maximizing Monotone Approximately Submodular Minus Modular Functions
Chao Qian
As evolutionary algorithms (EAs) are general-purpose optimization algorithms, recent theoretical studies have tried to analyze their performance for solving general problem classes…
On the Robustness of Median Sampling in Noisy Evolutionary Optimization
Chao Bian, Chao Qian, Yang Yu +1
Evolutionary algorithms (EAs) are a sort of nature-inspired metaheuristics, which have wide applications in various practical optimization problems. In these problems, objective ev…
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{…
Maximizing Submodular or Monotone Approximately Submodular Functions by Multi-objective Evolutionary Algorithms
Chao Qian, Yang Yu, Ke Tang +2
Evolutionary algorithms (EAs) are a kind of nature-inspired general-purpose optimization algorithm, and have shown empirically good performance in solving various real-word optimiz…
Running Time Analysis of the (1+1)-EA for OneMax and LeadingOnes under Bit-wise Noise
Chao Qian, Chao Bian, Wu Jiang +1
In many real-world optimization problems, the objective function evaluation is subject to noise, and we cannot obtain the exact objective value. Evolutionary algorithms (EAs), a ty…
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…