Glyph: Symbolic Regression Tools
This tool simplifies symbolic regression for non-expert programmers in experimental domains, but it is incremental as it builds on existing genetic programming methods.
The authors introduced Glyph, a Python package for genetic programming-based symbolic regression, designed to be accessible for domain experts in both numerical simulations and real-world experiments, with features like a ZeroMQ interface for task separation.
We present Glyph - a Python package for genetic programming based symbolic regression. Glyph is designed for usage let by numerical simulations let by 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 http://github.com/ambrosys/glyph . Domain experts are be 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.