Machine Learning produces efficient decision and prediction models based on input-output data only. Such models have the form of decision trees or neural nets and are far from transparent analytical models, based on mathematical formulas. Analytical model discovery requires additional knowledge and may be performed with Grammatical Evolution. Such models are transparent, concise, and have readable components and structure. This paper reports on a non-trivial experiment with generating such models.
View on arXiv@article{skrzyński2025_2505.12440,
title={ Model Discovery with Grammatical Evolution. An Experiment with Prime Numbers },
author={ Jakub Skrzyński and Dominik Sepioło and Antoni Ligęza },
journal={arXiv preprint arXiv:2505.12440},
year={ 2025 }
}