Enhancing regression models for complex systems using evolutionary techniques for feature engineering

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{Arroba:2015:grid,

• title = "Enhancing regression models for complex systems using evolutionary techniques for feature engineering",
• author = "Patricia Arroba and Jose Luis Risco-Martin and Marina Zapater and Jose Manuel Moya and Jose Luis Ayala",
• journal = "Journal of Grid Computing",
• year = "2015",
• volume = "13",
• number = "3",
• pages = "409--423",
• publisher = "Springer",
• month = sep # "~27",
• keywords = "genetic algorithms, genetic programming",
• URL = "http://eprints.ucm.es/30960/",
• URL = "http://eprints.ucm.es/30960/1/JGridComputing2014.pdf",
• URL = "http://link.springer.com/article/10.1007%2Fs10723-014-9313-8",
• ISSN = "1572-9184",
• DOI = "doi:10.1007/s10723-014-9313-8",
• abstract = "This work proposes an automatic methodology for modelling complex systems. Our methodology is based on the combination of Grammatical Evolution and classical regression to obtain an optimal set of features that take part of a linear and convex model. This technique provides both Feature Engineering and Symbolic Regression in order to infer accurate models with no effort or designer's expertise requirements. As advanced Cloud services are becoming mainstream, the contribution of data centers in the overall power consumption of modern cities is growing dramatically. These facilities consume from 10 to 100 times more power per square foot than typical office buildings. Modeling the power consumption for these infrastructures is crucial to anticipate the effects of aggressive optimisation policies, but accurate and fast power modelling is a complex challenge for high-end servers not yet satisfied by analytical approaches. For this case study, our methodology minimises error in power prediction. This work has been tested using real Cloud applications resulting on an average error in power estimation of 3.98percent. Our work improves the possibilities of deriving Cloud energy efficient policies in Cloud data centers being applicable to other computing environments with similar characteristics.",
• bibsource = "OAI-PMH server at eprints.ucm.es",
• language = "en",
• oai = "oai:www.ucm.es:30960",
• relation = "10.1007/s10723-014-9313-8; TIN2008-00508",
• rights = "info:eu-repo/semantics/openAccess",
• type = "PeerReviewed",

}

Genetic Programming entries for Patricia Arroba Jose L Risco-Martin Marina Zapater Jose Manuel Moya Jose Luis Ayala

Citations