Reinforcement learning based adaptive metaheuristics. (arXiv:2206.12233v2 [cs.NE] UPDATED)

Parameter adaptation, that is the capability to automatically adjust an
algorithm's hyperparameters depending on the problem being faced, is one of the
main trends in evolutionary computation applied to numerical optimization.
While several handcrafted adaptation policies have been proposed over the years
to address this problem, only few attempts have been done so far at applying
machine learning to learn such policies. Here, we introduce a general-purpose
framework for performing parameter adaptation in continuous-domain
metaheuristics based on state-of-the-art reinforcement learning …

arxiv learning reinforcement reinforcement learning