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A Federated Data-Driven Evolutionary Algorithm for Expensive Multi/Many-objective Optimization
[article]
2021
arXiv
pre-print
Data-driven optimization has found many successful applications in the real world and received increased attention in the field of evolutionary optimization. Most existing algorithms assume that the data used for optimization is always available on a central server for construction of surrogates. This assumption, however, may fail to hold when the data must be collected in a distributed way and is subject to privacy restrictions. This paper aims to propose a federated data-driven evolutionary
arXiv:2106.12086v1
fatcat:2cnpqii4rffcvkhqtttxrpg4ai