85 citations · 149 across the 20 of their papers we have counts for
20 papers
A Data-Driven Evolutionary Transfer Optimization for Expensive Problems in Dynamic Environments
Ke Li, Renzhi Chen, Xin Yao
Many real-world problems are usually computationally costly and the objective functions evolve over time. Data-driven, a.k.a. surrogate-assisted, evolutionary optimization has been…
Data-Driven Evolutionary Multi-Objective Optimization Based on Multiple-Gradient Descent for Disconnected Pareto Fronts
Renzhi Chen, Ke Li
Data-driven evolutionary multi-objective optimization (EMO) has been recognized as an effective approach for multi-objective optimization problems with expensive objective function…
Interactive Evolutionary Multi-Objective Optimization via Learning-to-Rank
Ke Li, Guiyu Lai, Xin Yao
In practical multi-criterion decision-making, it is cumbersome if a decision maker (DM) is asked to choose among a set of trade-off alternatives covering the whole Pareto-optimal f…
Unlocking the Secrets of Software Configuration Landscapes-Ruggedness, Accessibility, Escapability, and Transferability
Mingyu Huang, Peili Mao, Ke Li
Modern software systems are often highly configurable to tailor varied requirements from diverse stakeholders. Understanding the mapping between configurations and the desired perf…
Batched Data-Driven Evolutionary Multi-Objective Optimization Based on Manifold Interpolation
Ke Li, Renzhi Chen
Multi-objective optimization problems are ubiquitous in real-world science, engineering and design optimization problems. It is not uncommon that the objective functions are as a b…
Decomposition Multi-Objective Evolutionary Optimization: From State-of-the-Art to Future Opportunities
Ke Li
Decomposition has been the mainstream approach in the classic mathematical programming for multi-objective optimization and multi-criterion decision-making. However, it was not pro…