Model Discovery with Grammatical Evolution. An Experiment with Prime Numbers
This addresses the need for interpretable models in machine learning, though it appears incremental as it applies an existing method to a specific domain.
The paper tackled the problem of discovering transparent analytical models from data using Grammatical Evolution, reporting on an experiment with prime numbers to generate concise and readable formulas.
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.