MFEA-CoD coordinates novelty search tasks with repulsion and adaptive transfer to collaboratively discover diverse novel solutions across synthetic, maze, MuJoCo, and generative problems.
Ensemble of domain adaptation-based knowledge transfer for evolutionary multitasking
2 Pith papers cite this work. Polarity classification is still indexing.
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SEETO achieves 6% better hypervolume in NWP parameter calibration with only 20 evaluations by using meteorological state representations for bi-level knowledge transfer from similar past tasks.
citing papers explorer
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From Consistency to Collaborative Discovery: MFEA-CoD for Multitask Novelty Search
MFEA-CoD coordinates novelty search tasks with repulsion and adaptive transfer to collaboratively discover diverse novel solutions across synthetic, maze, MuJoCo, and generative problems.
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Efficient Parameter Calibration of Numerical Weather Prediction Models via Evolutionary Sequential Transfer Optimization
SEETO achieves 6% better hypervolume in NWP parameter calibration with only 20 evaluations by using meteorological state representations for bi-level knowledge transfer from similar past tasks.