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 Bayesian 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.


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.
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Jin, Ying, Weilin Fu, Jian Kang, Jiadong Guo, and Jian Guo. 2020. β€œBayesian Symbolic Regression.” arXiv:1910.08892 [Stat], January.
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Lehman, Joel, Jonathan Gordon, Shawn Jain, Kamal Ndousse, Cathy Yeh, and Kenneth O. Stanley. 2022. β€œEvolution Through Large Models.” arXiv.
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