A k-nearest-neighbor approach constructs problem-specific algorithm portfolios that outperform both single solvers and the virtual best solver in fixed-budget black-box optimization.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.NE 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
GeM-EA uses bi-level meta-learning for surrogate initialization and generative replay in a multi-island evolutionary strategy to achieve faster adaptation and robustness in streaming data-driven optimization under concept drift.
citing papers explorer
-
Similarity-based Portfolio Construction for Black-box Optimization
A k-nearest-neighbor approach constructs problem-specific algorithm portfolios that outperform both single solvers and the virtual best solver in fixed-budget black-box optimization.
-
GeM-EA: A Generative and Meta-learning Enhanced Evolutionary Algorithm for Streaming Data-Driven Optimization
GeM-EA uses bi-level meta-learning for surrogate initialization and generative replay in a multi-island evolutionary strategy to achieve faster adaptation and robustness in streaming data-driven optimization under concept drift.