MERSEM uses evolutionary reinforcement learning to allocate graph workloads in edge-cloud systems, reducing SLA violations by up to 45% and carbon emissions by up to 12% versus prior methods.
Montes de Oca, Thomas Stützle, and Marco Dorigo
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
FAmv modifies the Firefly Algorithm with a unified hybrid distance for mixed continuous-discrete spaces and matches or exceeds state-of-the-art methods on CEC2013 benchmarks and engineering problems.
citing papers explorer
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Sustainable Graph Analytics Workload Scheduling with Evolutionary Reinforcement Learning in Edge-Cloud Systems
MERSEM uses evolutionary reinforcement learning to allocate graph workloads in edge-cloud systems, reducing SLA violations by up to 45% and carbon emissions by up to 12% versus prior methods.
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A Firefly Algorithm for Mixed-Variable Optimization Based on Hybrid Distance Modeling
FAmv modifies the Firefly Algorithm with a unified hybrid distance for mixed continuous-discrete spaces and matches or exceeds state-of-the-art methods on CEC2013 benchmarks and engineering problems.