48 citations · 134 across the 20 of their papers we have counts for
6 papers · 1 filter
When to Use Which? Benchmarking Optimisers for Configurable Systems under Varying Budgets
Chao Jiang, Yulong Ye, Tao Chen +1
Software configuration tuning is crucial for optimising system performance, and various optimisers have emerged over the last decade. Yet, the time required during the tuning proce…
On Scalability of Multi-Objective Evolutionary Algorithms on Combinatorial Optimisation Problems
Menghao Tang, Zimin Liang, Miqing Li
Scalability of evolutionary algorithms refers to assessing how their performance changes as problem size increases. In the area of multi-objective optimisation, research on the sca…
Do We Really Need to Approach the Entire Pareto Front in Many-Objective Bayesian Optimisation?
Chao Jiang, Jingyu Huang, Miqing Li
Many-objective optimisation, a subset of multi-objective optimisation, involves optimisation problems with more than three objectives. As the number of objectives increases, the nu…
Revealing Domain-Spatiality Patterns for Configuration Tuning: Domain Knowledge Meets Fitness Landscapes
Yulong Ye, Hongyuan Liang, Chao Jiang +2
Configuration tuning for better performance is crucial in quality assurance. Yet, there has long been a mystery on tuners' effectiveness, due to the black-box nature of configurabl…
Variable Search Stepsize for Randomized Local Search in Multi-Objective Combinatorial Optimization
Xuepeng Ren, Maocai Wang, Guangming Dai +4
Over the past two decades, research in evolutionary multi-objective optimization has predominantly focused on continuous domains, with comparatively limited attention given to mult…
Random is Faster than Systematic in Multi-Objective Local Search
Zimin Liang, Miqing Li
Local search is a fundamental method in operations research and combinatorial optimisation. It has been widely applied to a variety of challenging problems, including multi-objecti…