Dream-COP: Guiding Algorithm Discovery for Combinatorial Optimization Problems in an Experience World

Dream-COP introduces a framework that uses large language models to discover algorithms for combinatorial optimization problems by replaying past search trajectories in an experience world. A policy agent learns to guide a discovery agent through dreaming, providing feedback on program quality and search steps. Experiments on five COPs show that Dream-COP outperforms fixed exploration, matching optimal values on the Knapsack Problem and reducing the OBPP gap while cutting search requests by 42.5%, and its policies transfer across different problems.

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