Latent Heuristic Search performs continuous optimization over learned embeddings of heuristics, using normalizing flows and LLM prompting to discover competitive solvers for TSP, CVRP, KSP, and OBP.
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3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
RECORD introduces multiplicity reduction, on-the-fly aggregation, refined dominance fixing, and a new divisibility bound to outperform COMBO and BOUKNAP by orders of magnitude on hard KP and BKP benchmark instances.
Energy-aware metaheuristics use an EI/J score to dynamically pick operators that maximize fitness gain per unit energy, reaching comparable fitness with substantially less energy than standard versions on knapsack, NK-landscapes, and error-correcting code problems.
citing papers explorer
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Latent Heuristic Search: Continuous Optimization for Automated Algorithm Design
Latent Heuristic Search performs continuous optimization over learned embeddings of heuristics, using normalizing flows and LLM prompting to discover competitive solvers for TSP, CVRP, KSP, and OBP.
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Solving Hard Instances from Knapsack and Bounded Knapsack Problems: A new state-of-the-art solver
RECORD introduces multiplicity reduction, on-the-fly aggregation, refined dominance fixing, and a new divisibility bound to outperform COMBO and BOUKNAP by orders of magnitude on hard KP and BKP benchmark instances.
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Energy-Aware Metaheuristics
Energy-aware metaheuristics use an EI/J score to dynamically pick operators that maximize fitness gain per unit energy, reaching comparable fitness with substantially less energy than standard versions on knapsack, NK-landscapes, and error-correcting code problems.