Glyph: Symbolic Regression Tools
Markus Quade, Julien Gout, Markus Abel
TL;DR
Glyph is a Python package that simplifies the application of genetic programming for symbolic regression, especially for experimentalists, by providing a user-friendly interface and remote evaluation capabilities.
Contribution
The paper introduces Glyph, a novel Python toolkit that enables easy use of genetic programming for symbolic regression in experimental and numerical contexts.
Findings
Glyph facilitates symbolic regression for experimentalists without programming expertise.
The ZeroMQ interface separates optimization from evaluation tasks.
Glyph is accessible via PyPI and Github for broad adoption.
Abstract
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.
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Taxonomy
TopicsEvolutionary Algorithms and Applications · Metaheuristic Optimization Algorithms Research · Reinforcement Learning in Robotics