Eureqa
Eureqa (pronounced "eureka") is a software tool for detecting equations and hidden mathematical relationships in your data. Its goal is to identify the simplest mathematical formulas which could describe the underlying mechanisms that produced the data. Eureqa is free to download and use. Below you will find the program download, video tutorial, user forum, and other and reference materials.
- Read about Eureqa at
, in the article "Download Your Own Robot Scientist" by Brandon Keim
- Read about Eureqa at
, in the article "Eureqa, the Robot Scientist" by Lin Edwards
- Read about Eureqa at
, in the article "Eureqa - Software to Replace Scientists" by Aaron Saenz
- Read about Eureqa at
, in the article "Move over, Einstein: Machines will take it from here" by Justin Mullins
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 0.93 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 |
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Watch the Video: |
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Visit the Eureqa Blog:
You can find more information, advanced techniques, and tips on using Eureqa at the Eureqa blog:
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.
Schmidt M., Lipson H. (2009) "Incorporating Expert Knowledge in Evolutionary Search: A Study of Seeding Methods," Genetic and Evolutionary Computation Conference (GECCO'09).
Schmidt M., Lipson H. (2009) "Solving Iterated Functions Using Genetic Programming," Genetic and Evolutionary Computation Conference, Late Breaking Paper (GECCO'09).
Schmidt M., Lipson H. (2007), "Learning Noise", Genetic and Evolutionary Computation Conference (GECCO'07), pp. 1680-1685.