Related work

The foundational work on continual learning, 1980 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

5 papers of 8,653Sort Recent · Most cited
  1. 2026
    Continual Evolution Strategies in Control TasksNICOLA PITZALIS, Eleni Nisioti, Antonio Carta … Andrea CossuGenetic and Evolutionary Computation Conference Companion · University of Pisa · IT University of Copenhagen
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  2. 2025
    Improving Deep Optimisation for the Multi-dimensional Knapsack Problem using Elastic Weight ConsolidationAntoine Calame, Ruth Misener, Joshua KnowlesGenetic and Evolutionary Computation Conference Companion · Imperial College London · University of Birmingham
  3. 2024
    Evolutionary Data Subset Selection for Class-Incremental Learning on Memory-Constrained SystemsEpifanios Baikas, Danesh Tarapore, David B. ThomasGenetic and Evolutionary Computation Conference Companion · University of Southampton
  4. 2019
    Towards continual reinforcement learning through evolutionary meta-learningDjordje Grbic, Sebastian RisiGenetic and Evolutionary Computation Conference Companion · IT University of Copenhagen
  5. 2018
    Embodiment can combat catastrophic forgettingJoshua Powers, Sam Kriegman, Josh BongardGenetic and Evolutionary Computation Conference Companion · University of Vermont
About this index

We keep this list because we read the field and wanted one place to see it. It covers work on continual learning itself, in the core areas of machine learning, and leaves out papers that apply it inside another field, such as medical imaging or fault diagnosis. By default it shows the papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or written by someone who has published there, or cited a few hundred times. The rest are one click away under “All papers”. It is seeded from the community lists kept by ContinualAI and by Xialei Liu, then filled out from OpenAlex, and every week a script looks for new papers on OpenAlex and arXiv. A model reads each candidate and decides whether it belongs; a person reviews the additions before they go live. Authors and affiliations come from OpenAlex, so a recent preprint can lack its institutions for a week or two.

Missing something, or filed under the wrong venue? Write to hello@unify.ai with the arXiv id or DOI.