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Kai Staats 2015-11-04 20:47:42 +02:00
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README.md
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# Karoo GP
Karoo GP is an evolutionary algorithm, a genetic programming application suite which provides both symbolic regression and classification analysis. Written in the programming language Python, Karoo GP owes its foundation to the “Field Guide to Genetic Programming” by Poli, Langdon, McPhee, and Koza.
Karoo GP is an evolutionary algorithm, a Genetic Programming application suite which provides both Symbolic Regression and Classification analysis. Written in the programming language Python, Karoo GP owes its foundation to the "Field Guide to Genetic Programming" by Poli, Langdon, McPhee, and Koza.
Karoo GP provides a transparent interface to the inner workings of genetic programming. As a teaching tool, it enables instructors to showcase, step-by-step, how an evolutionary algorithm arrives to its solution. As a hands-on learning tool, Karoo GP supports rapid, repeatable experimentation with a simple, no-programming-required interface. Included with Karoo GP are two executables: an intuitive Text-based User Interface with built-in, real-world test cases, and a fully scriptable, single-line configuration which provides SciKit Learn-like functionality.
Karoo GP provides a transparent interface to the inner workings of Genetic Programming. As a teaching tool, it enables instructors to showcase, step-by-step, how an evolutionary algorithm arrives to its solution. As a hands-on learning tool, Karoo GP supports rapid, repeatable experimentation with a simple, no-programming-required interface. Included with Karoo GP are two executables: an intuitive Text-based User Interface with built-in, real-world test cases, and a fully scriptable, single-line configuration which provides SciKit Learn-like functionality.
The Quick Start Tutorial (PDF) offers system requirements, a crash-course in Genetic Programming, and use of Karoo GP for both the novice and advanced user.

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RELEASE_NOTES.txt
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2015 11/04
Initial development of Karoo GP began in February 2015, on a Python-based evolutionary algorithm for an MSc research project at the University of Cape Town (UCT) / African Institute for Mathematical Sciences (AIMS) and the Square Kilometer Array (SKA). The myriad debug statements evolved into the user interface and the classic Machine Learning test cases into the built-in example runs.
Initial development of Karoo GP began in February 2015, on a Python-based evolutionary algorithm for an MSc research project at the University of Cape Town (UCT) / African Institute for Mathematical Sciences (AIMS) and the Square Kilometer Array (SKA). The myriad debug statements evolved into the user interface while the classic Machine Learning test cases became the built-in example runs.
In the end, Karoo GP became a fairly flexible, easy-to-use platform for Genetic Programming.
In the end, Karoo GP became a flexible, easy-to-use platform for Genetic Programming.
With all development conducted locally, this version 0.9 marks the first release to GitHub.
It has been thoroughly tested on a 40-core server at the Square Kilometer Array offices in Cape Town, South Africa, where for one month it worked on 10,000 row datasets for up to 50 hours without a single crash. It is proved as a fully functional, multi-core workhorse.
This initial release is private, with select collaborators only.
With all development to date conducted locally, this version 0.9 marks the first release to GitHub.
This initial GitHub release is private, shared with select collaborators only. Please do not distribute any part of the code until it is made public.
Thank you! --kai
Once a paper is published in conjunction with Karoo GP, the repository will be made public.