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Found 5 papers, 1 papers with code
Benchmarking Subset Selection from Large Candidate Solution Sets in Evolutionary Multi-objective Optimization
This paper aims to fill this research gap by proposing a benchmark test suite for subset selection from large candidate solution sets, and comparing some representative methods using the proposed test suite.
Evolutionary Multi-Objective Optimization Algorithm Framework with Three Solution Sets
The three solution sets are the main population of an EMO algorithm, an external archive to store promising solutions, and a final solution set which is presented to the decision maker.
Decomposition-Based Multi-Objective Evolutionary Algorithm Design under Two Algorithm Frameworks
In the final population framework, the final population of an EMO algorithm is presented to the decision maker.
Algorithm Configurations of MOEA/D with an Unbounded External Archive
In this framework, which is referred to as the solution selection framework, the final population does not have to be a good solution set.
Solution Subset Selection for Final Decision Making in Evolutionary Multi-Objective Optimization
The selection of a single final solution from the obtained solutions is assumed to be done by a human decision maker.
