- author = "Ilknur Icke and Joshua Bongard",
- title = "Modeling Hierarchy Using Symbolic Regression",
- booktitle = "2013 IEEE Conference on Evolutionary Computation",
- volume = "1",
- year = "2013",
- month = jun # " 20-23",
- editor = "Luis Gerardo {de la Fraga}",
- pages = "2980--2987",
- address = "Cancun, Mexico",
- keywords = "genetic algorithms, genetic programming, symbolic regression, SR, dependency graph, Naive SR, NSR, Hierarchical SR, HSR",
- isbn13 = "978-1-4799-0453-2",
- article_id = "1512",
-
DOI = "
10.1109/CEC.2013.6557932",
- size = "8 pages",
- abstract = "Symbolic regression (SR) is an attractive modeling approach because it can capture and present, mathematically, relationships between variables of interest. However, given n variables to model, symbolic regression returns a flat list of n equations. As the number of state variables to be modeled scales, interpretation of such a list becomes difficult. Here we present a symbolic regression method that detects and captures hidden hierarchy in a given system. The method returns the equations in a hierarchical dependency graph, which increases the interpretability of the results. We demonstrate that two variations of this hierarchical modeling approach outperform non-hierarchical symbolic regression on a synthetic data suite.",
-
notes = "Morphology, Evolution and Cognition Lab. Department of
Computer Science University of Vermont
CEC 2013 - A joint meeting of the IEEE, the EPS and the IET.",
