Description
Aging Evolution, or Regularized Evolution, is an evolutionary algorithm for neural architecture search. Whereas in tournament selection, the best architectures are kept, in aging evolution we associate each genotype with an age, and bias the tournament selection to choose the younger genotypes. In the context of architecture search, aging evolution allows us to explore the search space more, instead of zooming in on good models too early, as non-aging evolution would.
Papers Using This Method
Lessons from the Clustering Analysis of a Search Space: A Centroid-based Approach to Initializing NAS2021-08-20AgEBO-Tabular: Joint Neural Architecture and Hyperparameter Search with Autotuned Data-Parallel Training for Tabular Data2020-10-30Regularized Evolution for Image Classifier Architecture Search2018-02-05