- author = "Afsoon {Yousefi Zowj} and Josh C. Bongard and Christian Skalka",
- title = "A Genetic Programming Approach to Cost-Sensitive Control in Resource Constrained Sensor Systems",
- booktitle = "GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation",
- year = "2015",
- 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",
- isbn13 = "978-1-4503-3472-3",
- pages = "1295--1302",
- keywords = "genetic algorithms, genetic programming, Real World Applications",
- month = "11-15 " # jul,
- organisation = "SIGEVO",
- address = "Madrid, Spain",
-
URL = "
http://doi.acm.org/10.1145/2739480.2754751",
-
DOI = "
doi:10.1145/2739480.2754751",
- publisher = "ACM",
- publisher_address = "New York, NY, USA",
- 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.",
-
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)",
