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Forecasting continuous optimization algorithm performance improvement within each run

delete2026-04-28
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Peter Korošec
DOI：10.1016/j.swevo.2026.102399delete
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摘要

摘要

En 中文
• 内部运行模型预测每个问题实例迭代中的未来性能提升。 • 使用缩放种群数据，确保对简单变换的不变性。 • 整合序列种群动态以预测性能改进。 • 使用LSTM时间序列模型捕捉缩放优化数据中的趋势。 • LSTM在预测性能提升方面始终优于基线模型。
Keyword:
Black-box optimization
Single-objective continuous optimization
Forecasting performance improvement
Runtime forecasting
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Swarm and Evolutionary Computation 封面图
Swarm and Evolutionary Computation
IF:
8.5
论文数:
2.2K
被引数:
1.0W

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