85 citations · 149 across the 20 of their papers we have counts for
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Does Preference Always Help? A Holistic Study on Preference-Based Evolutionary Multi-Objective Optimisation Using Reference Points
Ke Li, Minhui Liao, Kalyanmoy Deb +2
The ultimate goal of multi-objective optimisation is to help a decision maker (DM) identify solution(s) of interest (SOI) achieving satisfactory trade-offs among multiple conflicti…
Bayesian Network Based Label Correlation Analysis For Multi-label Classifier Chain
Ran Wang, Suhe Ye, Ke Li +1
Classifier chain (CC) is a multi-label learning approach that constructs a sequence of binary classifiers according to a label order. Each classifier in the sequence is responsible…
Visualisation of Pareto Front Approximation: A Short Survey and Empirical Comparisons
Huiru Gao, Haifeng Nie, Ke Li
Visualisation is an effective way to facilitate the analysis and understanding of multivariate data. In the context of multi-objective optimisation, comparing to quantitative perfo…
Which Surrogate Works for Empirical Performance Modelling? A Case Study with Differential Evolution
Ke Li, Zilin Xiang, Kay Chen Tan
It is not uncommon that meta-heuristic algorithms contain some intrinsic parameters, the optimal configuration of which is crucial for achieving their peak performance. However, ev…