Christian S. Perone
Abstract
Pyevolve is an open-source framework for genetic algorithms. The initial long-term goal of the project was to create a complete and multi-platform framework for genetic algorithms in pure Python. However, the most recent developmental versions currently support also Genetic Programming (GP)[3]; accordingly, Pyevolve now aims at becoming a pure Python framework for evolutionary algorithms.
- David E. Goldberg. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Professional, January 1989. Google Scholar
Digital Library
- Python Software Foundation. Python success stories; also available in http://www.python.org/about/success/, 2009.Google Scholar
- John R. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection. Cambridge, MA: The MIT Press, 1992. Google ScholarDigital Library
- William B. Langdon Riccardo Poli and Nicholas Freitag McPhee. A field guide to genetic programming. Published via lulu.com and freely available at www.gp-field-guide.org.uk, with contributions by J. R. Koza, 2008. Google ScholarDigital Library
- Daniel G. Shafer. Python streamlines space shuttle mission design; also available in http://www.python.org/about/success/usa/, 2003.Google Scholar
- D. Whitley, K. Mathias, S. Rana, and J. Dzubera. Building better test functions. In Proceedings of the Sixth International Conference on Genetic Algorithms, pages 239--246. Morgan Kaufmann, 1995. Google ScholarDigital Library
Index Terms
- Pyevolve: a Python open-source framework for genetic algorithms
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