A Cross-Platform Assessment of Energy Consumption in Evolutionary Algorithms Towards Energy-Aware Bioinspired Algorithms

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Fernandez:2016:PPSN,
  author =       "F. {Fernandez de Vega} and F. Chavez and J. Diaz and 
                 J. A. Garcia and P. A. Castillo and Juan J. Merelo and 
                 C. Cotta",
  title =        "A Cross-Platform Assessment of Energy Consumption in
                 Evolutionary Algorithms Towards Energy-Aware
                 Bioinspired Algorithms",
  booktitle =    "14th International Conference on Parallel Problem
                 Solving from Nature",
  year =         "2016",
  editor =       "Julia Handl and Emma Hart and Peter R. Lewis and 
                 Manuel Lopez-Ibanez and Gabriela Ochoa and 
                 Ben Paechter",
  volume =       "9921",
  series =       "LNCS",
  address =      "Edinburgh",
  month =        "17-21 " # sep,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, Green
                 computing, Energy-aware computing, Performance
                 measurements, Evolutionary algorithms",
  isbn13 =       "978-3-319-45823-6",
  DOI =          "doi:10.1007/978-3-319-45823-6_51",
  size =         "10 pages",
  abstract =     "Energy consumption is a matter of paramount importance
                 in nowadays environmentally conscious society. It is
                 also bound to be a crucial issue in light of the
                 emergent computational environments arising from the
                 pervasive use of networked hand-held devices and
                 wearables. Evolutionary algorithms (EAs) are ideally
                 suited for this kind of environments due to their
                 intrinsic flexibility and adaptiveness, provided they
                 operate on viable energy terms. In this work we analyse
                 the energy requirements of EAs, and particularly one of
                 their main flavours, genetic programming (GP), on
                 several computational platforms and study the impact
                 that parametrisation has on these requirements, paving
                 the way for a future generation of energy-aware EAs. As
                 experimentally demonstrated, handheld devices and tiny
                 computer models mainly used for educational purposes
                 may be the most energy efficient ones when looking for
                 solutions by means of EAs.",
  notes =        "PPSN2016 http://ppsn2016.org",
}

Genetic Programming entries for Francisco Fernandez de Vega Francisco Chavez de la O Josefa Diaz Alvarez J A Garcia Pedro A Castillo Valdivieso Juan Julian Merelo Carlos Cotta

Citations