23 citations · 47 across the 11 of their papers we have counts for
11 papers · 1 filter
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…
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…
On the Combined Impact of Population Size and Sub-problem Selection in MOEA/D
Geoffrey Pruvost, Bilel Derbel, Arnaud Liefooghe +2
This paper intends to understand and to improve the working principle of decomposition-based multi-objective evolutionary algorithms. We review the design of the well-established M…