A graph neural network learns to approximate altruistic robot transfers across heterogeneous teams using Hamilton's rule, achieving near-optimal allocation in simulated firefighting scenarios.
Machine learning for combinato- rial optimization: A methodological tour d’horizon,
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Regression mapping from instance features to offline-tuned parameters improves Bilevel Late Acceptance Hill Climbing solutions by 0.28% on average over global tuning for the electric capacitated vehicle routing problem.
citing papers explorer
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Learning Altruistic Collaboration in Heterogeneous Multi-Team Systems
A graph neural network learns to approximate altruistic robot transfers across heterogeneous teams using Hamilton's rule, achieving near-optimal allocation in simulated firefighting scenarios.
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Instance-Aware Parameter Configuration in Bilevel Late Acceptance Hill Climbing for the Electric Capacitated Vehicle Routing Problem
Regression mapping from instance features to offline-tuned parameters improves Bilevel Late Acceptance Hill Climbing solutions by 0.28% on average over global tuning for the electric capacitated vehicle routing problem.