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.
@article{arxiv.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}
}
Comments
Presented during 5th Polish Conference on Artificial Intelligence, published in "PROGRESS IN POLISH ARTIFICIAL INTELLIGENCE RESEARCH 5" ISBN 978-83-8156-696-4