102 citations · 191 across the 11 of their papers we have counts for
11 papers
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…
Non-Elitist Evolutionary Multi-Objective Optimisation: Proof-of-Principle Results
Zimin Liang, Miqing Li, Per Kristian Lehre
Elitism, which constructs the new population by preserving best solutions out of the old population and newly-generated solutions, has been a default way for population update sinc…
Multi-Objective Archiving
Miqing Li, Manuel López-Ibáñez, Xin Yao
Most multi-objective optimisation algorithms maintain an archive explicitly or implicitly during their search. Such an archive can be solely used to store high-quality solutions pr…
Multi-agent Dynamic Algorithm Configuration
Ke Xue, Jiacheng Xu, Lei Yuan +4
Automated algorithm configuration relieves users from tedious, trial-and-error tuning tasks. A popular algorithm configuration tuning paradigm is dynamic algorithm configuration (D…
An Effective and Efficient Evolutionary Algorithm for Many-Objective Optimization
Yani Xue, Miqing Li, Xiaohui Liu
In evolutionary multiobjective optimization, effectiveness refers to how an evolutionary algorithm performs in terms of converging its solutions into the Pareto front and also dive…
How to Evaluate Solutions in Pareto-based Search-Based Software Engineering? A Critical Review and Methodological Guidance
Miqing Li, Tao Chen, Xin Yao
With modern requirements, there is an increasing tendency of considering multiple objectives/criteria simultaneously in many Software Engineering (SE) scenarios. Such a multi-objec…