10 citations · 21 across the 12 of their papers we have counts for
10 papers · 1 filter
Maintaining Diversity Provably Helps in Evolutionary Multimodal Optimization
Shengjie Ren, Zhijia Qiu, Chao Bian +2
In the real world, there exist a class of optimization problems that multiple (local) optimal solutions in the solution space correspond to a single point in the objective space. I…
An Archive Can Bring Provable Speed-ups in Multi-Objective Evolutionary Algorithms
Chao Bian, Shengjie Ren, Miqing Li +1
In the area of multi-objective evolutionary algorithms (MOEAs), there is a trend of using an archive to store non-dominated solutions generated during the search. This is because 1…
A First Running Time Analysis of the Strength Pareto Evolutionary Algorithm 2 (SPEA2)
Shengjie Ren, Chao Bian, Miqing Li +1
Evolutionary algorithms (EAs) have emerged as a predominant approach for addressing multi-objective optimization problems. However, the theoretical foundation of multi-objective EA…
Towards Running Time Analysis of Interactive Multi-objective Evolutionary Algorithms
Tianhao Lu, Chao Bian, Chao Qian
Evolutionary algorithms (EAs) are widely used for multi-objective optimization due to their population-based nature. Traditional multi-objective EAs (MOEAs) generate a large set of…
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…
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…
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{…
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…