Introduces an evolutionary population-based meta-RL method that maintains distinct meta-policies per weight vector to produce more diverse Pareto fronts in multi-objective supply chain optimisation.
A parallel global multiobjective framework for optimization: pagmo,
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
CHAMB-GA provides a microservice architecture with containers and a message broker to decouple genetic operations from fitness evaluations, enabling consistent scaling from small machines to over 3500 CPU cores on cloud and HPC systems for optimization problems.
citing papers explorer
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Meta-Reinforcement Learning via Evolution for Multi-Objective Combinatorial Supply Chain Optimisation
Introduces an evolutionary population-based meta-RL method that maintains distinct meta-policies per weight vector to produce more diverse Pareto fronts in multi-objective supply chain optimisation.
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CHAMB-GA: A Containerized HPC Scalable Microservice-Based Framework for Genetic Algorithms
CHAMB-GA provides a microservice architecture with containers and a message broker to decouple genetic operations from fitness evaluations, enabling consistent scaling from small machines to over 3500 CPU cores on cloud and HPC systems for optimization problems.