RACL lets a reasoning agent discover and apply control rules to a metaheuristic by observing operational memory and testing bounded interventions, shown on vehicle routing with reported cost improvements over baselines.
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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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RACL: Reasoning-Agent Control Layers for Continuous Metaheuristic Learning
RACL lets a reasoning agent discover and apply control rules to a metaheuristic by observing operational memory and testing bounded interventions, shown on vehicle routing with reported cost improvements over baselines.
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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.