22 citations · 34 across the 8 of their papers we have counts for
8 papers
Optimal Distribution of Solutions for Crowding Distance on Linear Pareto Fronts of Two-Objective Optimization Problems
Hisao Ishibuchi, Lie Meng Pang
Characteristics of an evolutionary multi-objective optimization (EMO) algorithm can be explained using its best solution set. For example, the best solution set for SMS-EMOA is the…
When to Truncate the Archive? On the Effect of the Truncation Frequency in Multi-Objective Optimisation
Zhiji Cui, Zimin Liang, Lie Meng Pang +2
Using an archive to store nondominated solutions found during the search of a multi-objective evolutionary algorithm (MOEA) is a useful practice. However, as nondominated solutions…
Large Language Model-Based Benchmarking Experiment Settings for Evolutionary Multi-Objective Optimization
Lie Meng Pang, Hisao Ishibuchi
When we manually design an evolutionary optimization algorithm, we implicitly or explicitly assume a set of target optimization problems. In the case of automated algorithm design,…
Benchmarking Subset Selection from Large Candidate Solution Sets in Evolutionary Multi-objective Optimization
Ke Shang, Tianye Shu, Hisao Ishibuchi +2
In the evolutionary multi-objective optimization (EMO) field, the standard practice is to present the final population of an EMO algorithm as the output. However, it has been shown…
Evolutionary Multi-Objective Optimization Algorithm Framework with Three Solution Sets
Hisao Ishibuchi, Lie Meng Pang, Ke Shang
It is assumed in the evolutionary multi-objective optimization (EMO) community that a final solution is selected by a decision maker from a non-dominated solution set obtained by a…
Decomposition-Based Multi-Objective Evolutionary Algorithm Design under Two Algorithm Frameworks
Lie Meng Pang, Hisao Ishibuchi, Ke Shang
The development of efficient and effective evolutionary multi-objective optimization (EMO) algorithms has been an active research topic in the evolutionary computation community. O…