- author = "Michael Schmidt and Hod Lipson",
- title = "Distilling Free-Form Natural Laws from Experimental Data",
- journal = "Science",
- year = "2009",
- volume = "324",
- number = "5923",
- pages = "81--85",
- month = "3 " # apr,
- keywords = "genetic algorithms, genetic programming",
-
URL = "
http://ccsl.mae.cornell.edu/sites/default/files/Science09_Schmidt.pdf",
-
DOI = "
doi:10.1126/science.1165893",
- size = "4.5 pages",
- abstract = "For centuries, scientists have attempted to identify and document analytical laws that underlie physical phenomena in nature. Despite the prevalence of computing power, the process of finding natural laws and their corresponding equations has resisted automation. A key challenge to finding analytic relations automatically is defining algorithmically what makes a correlation in observed data important and insightful. We propose a principle for the identification of nontriviality. We demonstrated this approach by automatically searching motion-tracking data captured from various physical systems, ranging from simple harmonic oscillators to chaotic double-pendula. Without any prior knowledge about physics, kinematics, or geometry, the algorithm discovered Hamiltonians, Lagrangians, and other laws of geometric and momentum conservation. The discovery rate accelerated as laws found for simpler systems were used to bootstrap explanations for more complex systems, gradually uncovering the alphabet used to describe those systems.",
-
notes = "Eureqa Pareto parsimony v. accuracy www.sciencemag.org
broken Feb 2019
http://www.sciencemag.org/cgi/data/324/5923/81/DC1/1
broken Feb 2019 http://www.sciencemag.org/content/vol324/issue5923/images/data/81/DC1/1165893s1.mpg 3mins 43seconds
broken Feb 2019 http://www.sciencemag.org/content/vol324/issue5923/images/data/81/DC1/invar_datasets.zip
Entered 2010 HUMIES GECCO 2010 http://www.genetic-programming.org/combined.php
