Please use this identifier to cite or link to this item: https://doi.org/10.2166/hydro.2009.041
Title: Introducing knowledge into learning based on genetic programming
Authors: Babovic, V. 
Keywords: Empirical equations
Genetic programming
Hydraulics
Sediment transport
Strong typing
Symbolic regression
Units of measurement
Issue Date: Jul-2009
Source: Babovic, V. (2009-07). Introducing knowledge into learning based on genetic programming. Journal of Hydroinformatics 11 (3-4) : 181-193. ScholarBank@NUS Repository. https://doi.org/10.2166/hydro.2009.041
Abstract: This work examines various methods for creating empirical equations on the basis of data while taking advantage of knowledge about the problem domain. It is demonstrated that the use of high level concepts aid in evolving equations that are easier to interpret by domain specialists. The application of the approach to real-world problems reveals that the utilization of such concepts results in equations with performance equal or superior to that of human experts. Finally, it is argued that the algorithm is best used as a hypothesis generator assisting scientists in the discovery process. © IWA Publishing 2009.
Source Title: Journal of Hydroinformatics
URI: http://scholarbank.nus.edu.sg/handle/10635/65728
ISSN: 14647141
DOI: 10.2166/hydro.2009.041
Appears in Collections:Staff Publications

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