# Generational Computation Reduction in Informal Counterexample-Driven Genetic Programming

arXiv: https://arxiv.org/abs/2408.12604 · HTML: https://mlsanity.com/paper/2408.12604/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 33 references that are in the map.
- First posted on arXiv 8 Aug 2024.

## Builds on (most cited first, 7 of 7)

- [Counterexample-Guided Learning of Monotonic Neural Networks](https://mlsanity.com/paper/2006.08852/) (2020)
- [Counterexample-Guided Data Augmentation](https://mlsanity.com/paper/1805.06962/) (2018)
- [Problem-solving benefits of down-sampled lexicase selection](https://mlsanity.com/paper/2106.06085/) (2021)
- [Informed Down-Sampled Lexicase Selection: Identifying productive training cases for efficient problem solving](https://mlsanity.com/paper/2301.01488/) (2023)
- [A Static Analysis of Informed Down-Samples](https://mlsanity.com/paper/2304.01978/) (2023)
- [An Exploration of Exploration: Measuring the ability of lexicase selection to find obscure pathways to optimality](https://mlsanity.com/paper/2107.09760/) (2021)
- [MTGP: Combining Metamorphic Testing and Genetic Programming](https://mlsanity.com/paper/2301.08665/) (2023)

## Abstract (arXiv)

Counterexample-driven genetic programming (CDGP) uses specifications provided as formal constraints to generate the training cases used to evaluate evolving programs. It has also been extended to combine formal constraints and user-provided training data to solve symbolic regression problems. Here we show how the ideas underlying CDGP can also be applied using only user-provided training data, without formal specifications. We demonstrate the application of this method, called ``informal CDGP,'' to software synthesis problems. Our results show that informal CDGP finds solutions faster (i.e. with fewer program executions) than standard GP. Additionally, we propose two new variants to informal CDGP, and find that one produces significantly more successful runs on about half of the tested problems. Finally, we study whether the addition of counterexample training cases to the training set is useful by comparing informal CDGP to using a static subsample of the training set, and find that the addition of counterexamples significantly improves performance.