• Corpus ID: 238419059

# Multi-objective Optimization by Learning Space Partitions

@article{Zhao2021MultiobjectiveOB,
title={Multi-objective Optimization by Learning Space Partitions},
author={Yiyang Zhao and Linnan Wang and Kevin Yang and Tianjun Zhang and Tian Guo and Yuandong Tian},
journal={ArXiv},
year={2021},
volume={abs/2110.03173}
}
• Published 7 October 2021
• Computer Science
• ArXiv
In contrast to single-objective optimization (SOO), multi-objective optimization (MOO) requires an optimizer to find the Pareto frontier, a subset of feasible solutions that are not dominated by other feasible solutions. In this paper, we propose LaMOO, a novel multi-objective optimizer that learns a model from observed samples to partition the search space and then focus on promising regions that are likely to contain a subset of the Pareto frontier. The partitioning is based on the dominance…
1 Citations

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