A Genetic Programming Approach to Cost-Sensitive Control in Resource Constrained Sensor Systems
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{YousefiZowj:2015:GECCO,
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author = "Afsoon {Yousefi Zowj} and Josh C. Bongard and
Christian Skalka",
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title = "A Genetic Programming Approach to Cost-Sensitive
Control in Resource Constrained Sensor Systems",
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booktitle = "GECCO '15: Proceedings of the 2015 Annual Conference
on Genetic and Evolutionary Computation",
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year = "2015",
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editor = "Sara Silva and Anna I Esparcia-Alcazar and
Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and
Christine Zarges and Luis Correia and Terence Soule and
Mario Giacobini and Ryan Urbanowicz and
Youhei Akimoto and Tobias Glasmachers and
Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and
Marta Soto and Carlos Cotta and Francisco B. Pereira and
Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and
Heike Trautmann and Jean-Baptiste Mouret and
Sebastian Risi and Ernesto Costa and Oliver Schuetze and
Krzysztof Krawiec and Alberto Moraglio and
Julian F. Miller and Pawel Widera and Stefano Cagnoni and
JJ Merelo and Emma Hart and Leonardo Trujillo and
Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and
Carola Doerr",
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isbn13 = "978-1-4503-3472-3",
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pages = "1295--1302",
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keywords = "genetic algorithms, genetic programming, Real World
Applications",
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month = "11-15 " # jul,
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organisation = "SIGEVO",
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address = "Madrid, Spain",
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URL = "http://doi.acm.org/10.1145/2739480.2754751",
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DOI = "doi:10.1145/2739480.2754751",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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abstract = "Resource constrained sensor systems are an
increasingly attractive option in a variety of
environmental monitoring domains, due to continued
improvements in sensor technology. However, sensors for
the same measurement application can differ in terms of
cost and accuracy, while fluctuations in environmental
conditions can impact both application requirements and
available energy. This raises the problem of
automatically controlling heterogeneous sensor suites
in resource constrained sensor system applications, in
a manner that balances cost and accuracy of available
sensors. We present a method that employs a hierarchy
of model ensembles trained by genetic programming (GP):
if model ensembles that poll low-cost sensors exhibit
too much prediction uncertainty, they automatically
transfer the burden of prediction to other GP-trained
model ensembles that poll more expensive and accurate
sensors. We show that, for increasingly challenging
datasets, this hierarchical approach makes predictions
with equivalent accuracy yet lower cost than a similar
yet non-hierarchical method in which a single
GP-generated model determines which sensors to poll at
any given time. Our results thus show that a hierarchy
of GP-trained ensembles can serve as a control
algorithm for heterogeneous sensor suites in resource
constrained sensor system applications that balances
cost and accuracy.",
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notes = "Also known as \cite{2754751}
See also \cite{zowj:2017:CIWSN}
GECCO-2015 A joint meeting of the twenty fourth
international conference on genetic algorithms
(ICGA-2015) and the twentith annual genetic programming
conference (GP-2015)",
- }
Genetic Programming entries for
Afsoon Yousefi Zowj
Josh C Bongard
Christian Skalka
Citations