Eureqa

Eureqa is a software tool for detecting equations and hidden mathematical relationships in your data. Its primary goal is to identify the simplest mathematical formulas which could describe the underlying mechanisms that produced the data. Eurequa is free to download and use. Below you will find the downloadable program, video tutorial, user forum, and other and reference materials.

If you publish work based on results generated by this program, please cite Schmidt M., Lipson H. (2009) "Distilling Free-Form Natural Laws from Experimental Data," Science, Vol. 324, no. 5923, pp. 81 - 85.

 Download the latest version, Eureqa version 0.74 beta, at the Download Page.

How to Use Eureqa: Instructions, video tutorial, and user forum

1) Paste/load in your data

2) Smooth the variables

3) Pick the search options

4) Start/monitor the search

5) View/analyze the solutions


Video:

Learn more about the technology behind Eureqa

General Information about Genetic Programming and Symbolic Regression

  Read about Symbolic Regression on Wikipedia

  Read an example Symbolic Regression problem by John Koza

  Read an overview of Symbolic Regression by Zelinka Ivan

Advanced Techniques Used in this Software

  Schmidt M., Lipson H. (2009) "Distilling Free-Form Natural Laws from Experimental Data," Science, Vol. 324, no. 5923, pp. 81 - 85. (see supplemental materials)

  Schmidt M., Lipson H. (2009) "Discovering a Domain Alphabet," Genetic and Evolutionary Computation Conference (GECCO'09), pp. 1083-1090.

  Schmidt, M., Lipson, H., (2008) "Coevolution of Fitness Predictors," IEEE Transactions on Evolutionary Computation, Vol.12, No.6, pp. 736-749.

  Schmidt M., Lipson H. (2008), "Data-mining Dynamical Systems: Automated Symbolic System Identification for Exploratory Analysis", Proceedings of the 9th Biennial ASME Conference on Engineering Systems Design and Analysis (ESDA08), Haifa, Israel, July 7-9, 2008.

  Schmidt M., Lipson H. (2007), "Comparison of Tree and Graph Encodings as Function of Problem Complexity", Genetic and Evolutionary Computation Conference (GECCO'07), pp. 1674-1679.

  Schmidt M., Lipson H. (2009), "Symbolic Regression of Implicit Equations," Genetic Programming Theory and Practice, Vol. 7, Chapter 5, pp. 73-85.