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Introduction

Grammatical Evolution (GE) is a population-based evolutionary algorithm, where a BNF-style grammar is used in the genotype to phenotype mapping process [O'Neill & Ryan, 2003].

PonyGE2 is an implementation of GE in Python. It's intended as an advertisement and a starting-point for those new to GE, a reference for students and researchers, a rapid-prototyping medium for our own experiments, and as a Python workout.

The original version of PonyGE (https://github.com/jmmcd/ponyge) was originally designed to be a small single-file implementation of GE. However, over time this has grown to a stage where a more formal structured approach was needed. This has led to the development of PonyGE2 (https://github.com/PonyGE/PonyGE2), presented here.

A technical paper describing PonyGE2 has been published and been made freely available on arXiv here.

PonyGE2 can be referenced using the following citation:

Fenton, M., McDermott, J., Fagan, D., Forstenlechner, S., Hemberg, E., and O'Neill, M. PonyGE2: Grammatical Evolution in Python. arXiv preprint, arXiv:1703.08535, 2017.

Requirements

We don't provide any setup.py script for now, so you cannot yet pip-install PonyGE2. PonyGE2 requires Python 3.5 (or higher) and it uses matplotlib, numpy, scipy, scikit-learn (sklearn), pandas, which can be installed as follows

pip install -r requirements.txt

All requirements can be satisfied with Anaconda.

Running PonyGE2

You can run an example problem (the default is regression, see below) just by typing:

$ cd src
$ python ponyge.py

This will run an example problem and generate a results folder. The folder contains several files showing the run's stats, producing graphs and documenting the parameters used, as well as a file detailing the best individual. For a more verbose command line experience run the following:

$ cd src
$ python ponyge.py --verbose

Each line of the verbose output corresponds to a generation in the evolution, and prints out all statistics on the current run (only if --verbose is specified). Upon completion of a run, the best individual is printed to the command line, along with summary statistics.

There are a number of arguments that can be used for passing values via the command-line. To see a full list of these just run the following:

$ python ponyge.py --help

About PonyGE2

Grammatical Evolution (GE) [O'Neill & Ryan, 2003] is a grammar-based form of Genetic Programming [Koza, 1992]. It marries principles from molecular biology to the representational power of formal grammars. GE’s rich modularity gives a unique flexibility, making it possible to use alternative search strategies, whether evolutionary, deterministic or some other approach, and to radically change its behaviour by merely changing the grammar supplied. As a grammar is used to describe the structures that are generated by GE, it is trivial to modify the output structures by simply editing the plain text grammar. This is one of the main advantages that makes the GE approach so attractive. The genotype-phenotype mapping also means that instead of operating exclusively on solution trees, as in standard GP, GE allows search operators to be performed on the genotype (e.g., integer or binary chromosomes), in addition to partially derived phenotypes, and the fully formed phenotypic derivation trees themselves.

PonyGE2 is primarily a Python implementation of canonical Grammatical Evolution, but it also includes a number of other popular techniques and EC aspects.

Documentation

For full documentation of PonyGE2, see the wiki.

Contact

The PonyGE2 development team can be contacted via GitHub.

License

PonyGE2 is hereby licensed for use under the GNU General Public License v3.0. See the file LICENSE for more details. PonyGE2 is copyright (C) 2009-2017.

References

  • Michael O'Neill and Conor Ryan, "Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language", Kluwer Academic Publishers, 2003.

  • Koza, J.R., 1992. "Genetic programming: on the programming of computers by means of natural selection (Vol. 1)". MIT press.