Genetic programming

December 22, 2015 — August 2, 2023

probabilistic algorithms

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 naïvely 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.

1 Incoming

Figure 2

2 References

Atkinson, Subber, and Wang. 2019. “Data-Driven Discovery of Free-Form Governing Differential Equations.” In.
Bown, and Lexer. 2006. Continuous-Time Recurrent Neural Networks for Generative and Interactive Musical Performance.” In Applications of Evolutionary Computing. Lecture Notes in Computer Science 3907.
Brauer, Holder, Dries, et al. 2002. Genetic Algorithms and Parallel Processing in Maximum-Likelihood Phylogeny Inference.” Molecular Biology and Evolution.
Collins. 2002. Experiments with a New Customisable Interactive Evolution Framework.” Organized Sound.
Floreano, and Mattiussi. 2008. Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies (Intelligent Robotics and Autonomous Agents).
Genetic Programming. 2000.
Jin, Fu, Kang, et al. 2020. Bayesian Symbolic Regression.” arXiv:1910.08892 [Stat].
Koza. 1992. Genetic Programming: On the Programming of Computers by Means of Natural Selection (Complex Adaptive Systems).
Lehman. 2007. “Evolution Through the Search for Novelty.”
Lehman, Gordon, Jain, et al. 2022. Evolution Through Large Models.”
Lehman, and Stanley. 2011. Abandoning Objectives: Evolution Through the Search for Novelty Alone.” Evolutionary Computation.
Levy. 1993. Artificial Life: A Report from the Frontier Where Computers Meet Biology.
Mitchell. 1996. An Introduction to Genetic Algorithms.
Mitchell, Hraber, and Crutchfield. 1993. Revisiting the Edge of Chaos: Evolving Cellular Automata to Perform Computations.” arXiv:adap-Org/9303003.
Poli, Langdon, and McPhee. 2008. A Field Guide to Genetic Programming.
Poli, Vanneschi, Langdon, et al. 2010. Theoretical Results in Genetic Programming: The Next Ten Years? Genetic Programming and Evolvable Machines.
Stanley. 2007. Compositional Pattern Producing Networks: A Novel Abstraction of Development.” Genetic Programming and Evolvable Machines.
Vanchurin, Wolf, Katsnelson, et al. 2021. Towards a Theory of Evolution as Multilevel Learning.”
Whitley, Starkweather, and Bogart. 1990. Genetic Algorithms and Neural Networks: Optimizing Connections and Connectivity.” Parallel Computing.
Zhang, Lehman, Stanley, et al. 2023. OMNI: Open-Endedness via Models of Human Notions of Interestingness.”