45 citations · 56 across the 5 of their papers we have counts for
5 papers
A Large Population Size Can Be Unhelpful in Evolutionary Algorithms
Tianshi Chen, Ke Tang, Guoliang Chen +1
The utilization of populations is one of the most important features of evolutionary algorithms (EAs). There have been many studies analyzing the impact of different population siz…
On the Easiest and Hardest Fitness Functions
Jun He, Tianshi Chen, Xin Yao
The hardness of fitness functions is an important research topic in the field of evolutionary computation. In theory, the study can help understanding the ability of evolutionary a…
Scaling Up Estimation of Distribution Algorithms For Continuous Optimization
Weishan Dong, Tianshi Chen, Peter Tino +1
Since Estimation of Distribution Algorithms (EDA) were proposed, many attempts have been made to improve EDAs' performance in the context of global optimization. So far, the studie…
Novel Analysis of Population Scalability in Evolutionary Algorithms
Jun He, Tianshi Chen, Boris Mitavskiy
Population-based evolutionary algorithms (EAs) have been widely applied to solve various optimization problems. The question of how the performance of a population-based EA depends…
The Impact of Mutation Rate on the Computation Time of Evolutionary Dynamic Optimization
Tianshi Chen, Yunji Chen, Ke Tang +2
Mutation has traditionally been regarded as an important operator in evolutionary algorithms. In particular, there have been many experimental studies which showed the effectivenes…