mlsanityData to 14 Oct 2026

Particularity

arXiv 2306.06812 ↗First posted 12 Jun 2023cs.NE cs.AI cs.LGcited by 3

Not on the map. Why?

Abstract (arXiv)

We describe a design principle for adaptive systems under which adaptation is driven by particular challenges that the environment poses, as opposed to average or otherwise aggregated measures of performance over many challenges. We trace the development of this "particularity" approach from the use of lexicase selection in genetic programming to "particularist" approaches to other forms of machine learning and to the design of adaptive systems more generally.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 15 of its 41 references that are in the map. First posted on arXiv 12 Jun 2023.

Builds on

15 of 41 references.

References · 15

  1. 2015Illuminating search spaces by mapping elitescited by 425
  2. 2018Where are we now? A large benchmark study of recent symbolic regression methodscited by 44
  3. 2019Epsilon-Lexicase Selection for Regressioncited by 31
  4. 2021PSB2: The Second Program Synthesis Benchmark Suitecited by 9
  5. 2019Lexicase selection in Learning Classifier Systemscited by 8
  6. 2021Problem-solving benefits of down-sampled lexicase selectioncited by 8
  7. 2022Lexicase Selection at Scalecited by 5
  8. 2023Informed Down-Sampled Lexicase Selection: Identifying productive training cases for efficient problem solvingcited by 5
  9. 2023Calculating lexicase selection probabilities is NP-Hardcited by 3
  10. 2023Probabilistic Lexicase Selectioncited by 3
  11. 2023A Static Analysis of Informed Down-Samplescited by 2
  12. 2022The Environmental Discontinuity Hypothesis for Down-Sampled Lexicase Selectioncited by 1
3 more
  1. 2023Untangling the Effects of Down-Sampling and Selection in Genetic Programmingcited by 1
  2. 2023Can the Problem-Solving Benefits of Quality Diversity Be Obtained Without Explicit Diversity Maintenance?cited by 1
  3. 2023Improving Recommendation System Serendipity Through Lexicase Selectioncited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 0; 2026: 1.

3 papers.

  1. 2023Quality Diversity through Human Feedback: Towards Open-Ended Diversity-Driven Optimizationcited by 10
  2. 2026Vector Policy Optimization: Training for Diversity Improves Test-Time Searchcited by 8
  3. 2024DALex: Lexicase-like Selection via Diverse Aggregationcited by 3