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Symbolic Regression for Data Modeling

**Overview**
Symbolic Regression allows domain experts to create, add modify rules or policies extracted from data. The most commonly used algorithms used in Symbolic Regression are:

- Genetic Algorithms

- Learning Classifiers Systems

Symbolic regression is used in many application ranging from network performance optimization, predicting failure (MTBF), streaming data to detecting security breaches.

**Presentation**
The following presentation describes the main components, benefits and drawbacks of symbolic regression.

**References
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Genetic Programming: on the Programming of Computers by Means of Natural Selection - J. Koza - MIT Press 1992
Reinforcement Learning: An introduction (Adaptive Computation and Machine learning) - R. Sutton, A. Barto - MIT Press 1998
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ReplyDeleteI am glad to see that more and more people are becoming interested in Symbolic Regression -- it is an algorithm that does the same job as a neural network, but in an explicit and simple way.

ReplyDeleteYou might find the code TuringBot interesting, it implements a symbolic regression optimization that uses the simulated annealing algorithm:

https://turingbotsoftware.com/