# Lexicase Selection at Scale

arXiv: https://arxiv.org/abs/2208.10719 · HTML: https://mlsanity.com/paper/2208.10719/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 29 references that are in the map.
- First posted on arXiv 23 Aug 2022.

## Builds on (most cited first, 8 of 8)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Epsilon-Lexicase Selection for Regression](https://mlsanity.com/paper/1905.13266/) (2019)
- [Lexicase selection in Learning Classifier Systems](https://mlsanity.com/paper/1907.04736/) (2019)
- [Genetic programming approaches to learning fair classifiers](https://mlsanity.com/paper/2004.13282/) (2020)
- [Lexicase Selection of Specialists](https://mlsanity.com/paper/1905.09372/) (2019)
- [Evolving Neural Selection with Adaptive Regularization](https://mlsanity.com/paper/2204.01662/) (2022)
- [Batch Tournament Selection for Genetic Programming](https://mlsanity.com/paper/1904.08658/) (2019)
- [Exploiting Tournament Selection for Efficient Parallel Genetic Programming](https://mlsanity.com/paper/1809.07406/) (2018)

## Built on by (most cited first, 5 of 5)

- [Pareto-Optimal Learning from Preferences with Hidden Context](https://mlsanity.com/paper/2406.15599/) (2024)
- [Probabilistic Lexicase Selection](https://mlsanity.com/paper/2305.11681/) (2023)
- [Particularity](https://mlsanity.com/paper/2306.06812/) (2023)
- [DALex: Lexicase-like Selection via Diverse Aggregation](https://mlsanity.com/paper/2401.12424/) (2024)
- [Ever Evolving Evaluator (EV3): Towards Flexible and Reliable Meta-Optimization for Knowledge Distillation](https://mlsanity.com/paper/2310.18893/) (2023)

## Abstract (arXiv)

Lexicase selection is a semantic-aware parent selection method, which assesses individual test cases in a randomly-shuffled data stream. It has demonstrated success in multiple research areas including genetic programming, genetic algorithms, and more recently symbolic regression and deep learning. One potential drawback of lexicase selection and its variants is that the selection procedure requires evaluating training cases in a single data stream, making it difficult to handle tasks where the evaluation is computationally heavy or the dataset is large-scale, e.g., deep learning. In this work, we investigate how the weighted shuffle methods can be employed to improve the efficiency of lexicase selection. We propose a novel method, fast lexicase selection, which incorporates lexicase selection and weighted shuffle with partial evaluation. Experiments on both classic genetic programming and deep learning tasks indicate that the proposed method can significantly reduce the number of evaluation steps needed for lexicase selection to select an individual, improving its efficiency while maintaining the performance.