Knowledge-Based Dynamic Systems Modeling: A Case Study on Modeling River Water Quality
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @InProceedings{Park:2021:ICDE,
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author = "Namyong Park and MinHyeok Kim and Nguyen Xuan Hoai and
R. I. {Bob McKay} and Dong-Kyun Kim",
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title = "Knowledge-Based Dynamic Systems Modeling: A Case Study
on Modeling River Water Quality",
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booktitle = "2021 IEEE 37th International Conference on Data
Engineering (ICDE)",
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year = "2021",
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pages = "2231--2236",
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abstract = "Modeling real-world phenomena is a focus of many
science and engineering efforts, from ecological
modeling to financial forecasting. Building an accurate
model for complex and dynamic systems improves
understanding of underlying processes and leads to
resource efficiency. Knowledge-driven modeling builds a
model based on human expertise, yet is often
suboptimal. At the opposite extreme, data-driven
modeling learns a model directly from data, requiring
extensive data and potentially generating overfitting.
We focus on an intermediate approach, model revision,
in which prior knowledge and data are combined to
achieve the best of both worlds. We propose a genetic
model revision framework based on tree-adjoining
grammar (TAG) guided genetic programming (GP), using
the TAG formalism and GP operators in an effective
mechanism making data-driven revisions while
incorporating prior knowledge. Our framework is
designed to address the high computational cost of
evolutionary modeling of complex systems. Via a case
study on the challenging problem of river water quality
modeling, we show that the framework efficiently learns
an interpretable model, with higher modeling accuracy
than existing methods.",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/ICDE51399.2021.00229",
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ISSN = "2375-026X",
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month = apr,
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notes = "Also known as \cite{9458939}",
- }
Genetic Programming entries for
Namyong Park
MinHyeok Kim
Nguyen Xuan Hoai
R I (Bob) McKay
Dong-Kyun Kim
Citations