A grammatical evolution approach for software effort estimation

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

@InProceedings{Barros:2013:GECCO,
  author =       "Rodrigo C. Barros and Marcio P. Basgalupp and 
                 Ricardo Cerri and Tiago S. {da Silva} and 
                 Andre C. P. L. F. {de Carvalho}",
  title =        "A grammatical evolution approach for software effort
                 estimation",
  booktitle =    "GECCO '13: Proceeding of the fifteenth annual
                 conference on Genetic and evolutionary computation
                 conference",
  year =         "2013",
  editor =       "Christian Blum and Enrique Alba and Anne Auger and 
                 Jaume Bacardit and Josh Bongard and Juergen Branke and 
                 Nicolas Bredeche and Dimo Brockhoff and 
                 Francisco Chicano and Alan Dorin and Rene Doursat and 
                 Aniko Ekart and Tobias Friedrich and Mario Giacobini and 
                 Mark Harman and Hitoshi Iba and Christian Igel and 
                 Thomas Jansen and Tim Kovacs and Taras Kowaliw and 
                 Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and 
                 John McCall and Alberto Moraglio and 
                 Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and 
                 Gustavo Olague and Yew-Soon Ong and 
                 Michael E. Palmer and Gisele Lobo Pappa and 
                 Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and 
                 Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and 
                 Daniel Tauritz and Leonardo Vanneschi",
  isbn13 =       "978-1-4503-1963-8",
  pages =        "1413--1420",
  keywords =     "genetic algorithms, genetic programming",
  month =        "6-10 " # jul,
  organisation = "SIGEVO",
  address =      "Amsterdam, The Netherlands",
  DOI =          "doi:10.1145/2463372.2463546",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Software effort estimation is an important task within
                 software engineering. It is widely used for planning
                 and monitoring software project development as a means
                 to deliver the product on time and within budget.
                 Several approaches for generating predictive models
                 from collected metrics have been proposed throughout
                 the years. Machine learning algorithms, in particular,
                 have been widely-employed to this task, bearing in mind
                 their capability of providing accurate predictive
                 models for the analysis of project stakeholders. In
                 this paper, we propose a grammatical evolution approach
                 for software metrics estimation. Our novel algorithm,
                 namely SEEGE, is empirically evaluated on public
                 project data sets, and we compare its performance with
                 state-of-the-art machine learning algorithms such as
                 support vector machines for regression and artificial
                 neural networks, and also to popular linear regression.
                 Results show that SEEGE outperforms the other
                 algorithms considering three different evaluation
                 measures, clearly indicating its effectiveness for the
                 effort estimation task.",
  notes =        "Also known as \cite{2463546} GECCO-2013 A joint
                 meeting of the twenty second international conference
                 on genetic algorithms (ICGA-2013) and the eighteenth
                 annual genetic programming conference (GP-2013)",
}

Genetic Programming entries for Rodrigo C Barros Marcio Porto Basgalupp Ricardo Cerri Tiago S da Silva Andre Ponce de Leon F de Carvalho

Citations