Model Discovery with Grammatical Evolution. An Experiment with Prime Numbers

TL;DR

This paper explores using Grammatical Evolution to discover transparent, analytical models from data, demonstrated through an experiment involving prime numbers, contrasting with traditional machine learning models.

Contribution

It presents an experimental application of Grammatical Evolution for analytical model discovery, highlighting its transparency and interpretability advantages.

Findings

Generated models are transparent and concise

Experiment successfully identified models related to prime numbers

Demonstrates feasibility of analytical model discovery with Grammatical Evolution

Abstract

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.