Energy-consumption prediction of genetic programming algorithms using a fuzzy rule-based system

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

@InProceedings{Chavez:2018:GECCOcomp,
  author =       "F. Chavez and F. {Fdez de Vega} and J. Diaz and 
                 J. A. Garcia and F. J. Rodriguez and P. A. Castillo",
  title =        "Energy-consumption prediction of genetic programming
                 algorithms using a fuzzy rule-based system",
  booktitle =    "GECCO '18: Proceedings of the Genetic and Evolutionary
                 Computation Conference Companion",
  year =         "2018",
  editor =       "Carlos Cotta and Tapabrata Ray and Hisao Ishibuchi and 
                 Shigeru Obayashi and Bogdan Filipic and 
                 Thomas Bartz-Beielstein and Grant Dick and 
                 Masaharu Munetomo and Silvino {Fernandez Alzueta} and Thomas Stuetzle and 
                 Pablo Valledor Pellicer and Manuel Lopez-Ibanez and 
                 Daniel R. Tauritz and Pietro S. Oliveto and 
                 Thomas Weise and Borys Wrobel and Ales Zamuda and 
                 Anne Auger and Julien Bect and Dimo Brockhoff and 
                 Nikolaus Hansen and Rodolphe {Le Riche} and Victor Picheny and 
                 Bilel Derbel and Ke Li and Hui Li and Xiaodong Li and 
                 Saul Zapotecas and Qingfu Zhang and Stephane Doncieux and 
                 Richard Duro and Joshua Auerbach and 
                 Harold {de Vladar} and Antonio J. Fernandez-Leiva and JJ Merelo and 
                 Pedro A. Castillo-Valdivieso and David Camacho-Fernandez and 
                 Francisco {Chavez de la O} and Ozgur Akman and 
                 Khulood Alyahya and Juergen Branke and Kevin Doherty and 
                 Jonathan Fieldsend and Giuseppe Carlo Marano and 
                 Nikos D. Lagaros and Koichi Nakayama and Chika Oshima and 
                 Stefan Wagner and Michael Affenzeller and 
                 Boris Naujoks and Vanessa Volz and Tea Tusar and Pascal Kerschke and 
                 Riyad Alshammari and Tokunbo Makanju and 
                 Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and 
                 John R. Woodward and Shin Yoo and John McCall and 
                 Nayat Sanchez-Pi and Luis Marti and Danilo Vasconcellos and 
                 Masaya Nakata and Anthony Stein and 
                 Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and 
                 Gabriela Ochoa and Stephen L. Smith and Stefano Cagnoni and 
                 Robert M. Patton and William {La Cava} and 
                 Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and 
                 Ivanoe {De Falco} and Antonio {Della Cioppa} and 
                 Ernesto Tarantino and Umberto Scafuri and P. G. M. Baltus and 
                 Giovanni Iacca and Ahmed Hallawa and Anil Yaman and 
                 Alma Rahat and Handing Wang and Yaochu Jin and 
                 David Walker and Richard Everson and Akira Oyama and 
                 Koji Shimoyama and Hemant Kumar and Kazuhisa Chiba and 
                 Pramudita Satria Palar",
  isbn13 =       "978-1-4503-5764-7",
  pages =        "9--10",
  address =      "Kyoto, Japan",
  DOI =          "doi:10.1145/3205651.3208216",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        "15-19 " # jul,
  organisation = "SIGEVO",
  keywords =     "genetic algorithms, genetic programming, Energy
                 Consumption, Raspberry-Pi, Laptop, Tablet",
  abstract =     "Energy awareness has gained momentum over the last
                 decade in the software industry, as well as in
                 environmentally concious society. Thus, algorithm
                 designers and programmers are paying increasing
                 attention this issue, particularly when hand-held
                 devices are considered, given their battery-consuming
                 characteristics. When we focus on Evolutionary
                 Algorithms, few works have attempted to study the
                 relationship between the main features of the
                 algorithm, the problem to be solved and the underlying
                 hardware where it runs. This work presents a
                 preliminary analysis and modelling of energy
                 consumption of EAs. We try to predict it by means of a
                 fuzzy rule-based system, so that different devices are
                 considered as well as a number of problems and Genetic
                 Programming parameters. Experimental results performed
                 show that the proposed model can predict energy
                 consumption with very low error values.",
  notes =        "Also known as \cite{3208216} GECCO-2018 A
                 Recombination of the 27th International Conference on
                 Genetic Algorithms (ICGA-2018) and the 23rd Annual
                 Genetic Programming Conference (GP-2018)",
}

Genetic Programming entries for Francisco Chavez de la O Francisco Fernandez de Vega Josefa Diaz Alvarez Jose Antonio Garcia Munoz Francisco Javier Rodriguez Diaz Pedro A Castillo Valdivieso

Citations