Glyph: Symbolic Regression Tools

Abstract

We present Glyph – a Python package for genetic programming based symbolic regression. Glyph is designed for usage in numerical simulations as well as real world experiments. For experimentalists, glyph-remote provides a separation of tasks: a ZeroMQ interface splits the genetic programming optimization task from the evaluation of an experimental (or numerical) run. Glyph can be accessed at https://github.com/Ambrosys/glyph. Domain experts are able to employ symbolic regression in their experiments with ease, even if they are not expert programmers. The reuse potential is kept high by a generic interface design. Glyph is available on PyPI and Github. Funding statement: This work has been partially supported by the German Science Foundation via SFB 880. MQ was supported by a fellowship within the FITweltweit program of the German Academic Exchange Service (DAAD).

Keywords

• Symbolic Regression
• Genetic Programming
• Machine Learning Control
• MOGP
• Python