Genetic programming



A nature-inspired approach to computing that attempts to mimic evolution for code. This method has fallen out of favour of late because it is just not very good in practice, (e.g. Brauer et al. 2002) at least as naΓ―vely implemented. The kind of problems that it seems like it might to solve, symbolic regression, have alternative that do pretty good, like neural automata, neural transformers, or Baysian Symbolic regressions (Jin et al. 2020).

Nonetheless there is some interesting theory here, some interesting history and it is possibly the right tool for some jobs.

To consider, connection to adversarial learning, connections to optimisation theory, particle filters, importance sampling…

Hence, this notebook.

TBC, maybe.

References

Atkinson, Steven, Waad Subber, and Liping Wang. 2019. β€œData-Driven Discovery of Free-Form Governing Differential Equations.” In, 7.
Bown, Oliver, and Sebastian Lexer. 2006. β€œContinuous-Time Recurrent Neural Networks for Generative and Interactive Musical Performance.” In Applications of Evolutionary Computing, edited by Franz Rothlauf, JΓΌrgen Branke, Stefano Cagnoni, Ernesto Costa, Carlos Cotta, Rolf Drechsler, Evelyne Lutton, et al., 652–63. Lecture Notes in Computer Science 3907. Springer Berlin Heidelberg.
Brauer, Matthew J., Mark T. Holder, Laurie A. Dries, Derrick J. Zwickl, Paul O. Lewis, and David M. Hillis. 2002. β€œGenetic Algorithms and Parallel Processing in Maximum-Likelihood Phylogeny Inference.” Molecular Biology and Evolution 19 (10): 1717–26.
Collins, Nick. 2002. β€œExperiments with a New Customisable Interactive Evolution Framework.” Organized Sound 7: 267–73.
Floreano, Dario, and Claudio Mattiussi. 2008. Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies (Intelligent Robotics and Autonomous Agents). The MIT Press.
Genetic Programming. 2000. Vol. 1802. Springer Berlin / Heidelberg.
Jin, Ying, Weilin Fu, Jian Kang, Jiadong Guo, and Jian Guo. 2020. β€œBayesian Symbolic Regression.” arXiv:1910.08892 [Stat], January.
Koza, John R. 1992. Genetic Programming: On the Programming of Computers by Means of Natural Selection (Complex Adaptive Systems). Cambridge, Mass.: The MIT Press.
Levy, Steven. 1993. Artificial Life: A Report from the Frontier Where Computers Meet Biology. 1st Vintage Books ed. New York: Vintage Books.
Mitchell, Melanie. 1996. An Introduction to Genetic Algorithms. The MIT Press.
Mitchell, Melanie, Peter Hraber, and James P. Crutchfield. 1993. β€œRevisiting the Edge of Chaos: Evolving Cellular Automata to Perform Computations.” arXiv:adap-Org/9303003, March.
Poli, Riccardo, William B Langdon, and Nicholas F McPhee. 2008. A Field Guide to Genetic Programming. Lulu Enterprises, UK Ltd.
Poli, Riccardo, Leonardo Vanneschi, William B Langdon, and Nicholas McPhee. 2010. β€œTheoretical Results in Genetic Programming: The Next Ten Years?” Genetic Programming and Evolvable Machines 11: 285-320-320.
Stanley, Kenneth O. 2007. β€œCompositional Pattern Producing Networks: A Novel Abstraction of Development.” Genetic Programming and Evolvable Machines 8 (2): 131–62.
Vanchurin, Vitaly, Yuri I. Wolf, Mikhail Katsnelson, and Eugene V. Koonin. 2021. β€œTowards a Theory of Evolution as Multilevel Learning.” Cold Spring Harbor Laboratory.
Whitley, D, T Starkweather, and C Bogart. 1990. β€œGenetic Algorithms and Neural Networks: Optimizing Connections and Connectivity.” Parallel Computing 14 (3): 347–61.

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