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This paper proposes Latent Heuristic Search (LHS), a framework that shifts heuristic discovery to a learned continuous latent manifold, using gradient-based optimization and normalizing flows to generate novel heuristics conditioned on large language models, achieving competitive results on TSP, CVRP, KSP, and Online Bin Packing.
This paper introduces HMACE, a heterogeneous multi-agent collaborative evolution framework that uses Large Language Models to automate heuristic design for NP-hard combinatorial optimization problems. It demonstrates improved quality-efficiency trade-offs over single-agent and multi-agent baselines on problems like TSP and BPP.