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Found 3 papers, 1 papers with code
Learning from Offline and Online Experiences: A Hybrid Adaptive Operator Selection Framework
A novel hybrid framework that learns to dynamically and adaptively select promising search operators is proposed.
Local Optima Correlation Assisted Adaptive Operator Selection
In this paper, we propose to empirically analyse the relationship between operators in terms of the correlation between their local optima and develop a measure for quantifying their relationship.
Evolving Constrained Reinforcement Learning Policy
Evolutionary algorithms have been used to evolve a population of actors to generate diverse experiences for training reinforcement learning agents, which helps to tackle the temporal credit assignment problem and improves the exploration efficiency.
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