Single and Multi Objective Genetic Programming for software development effort estimation

Created by W.Langdon from gp-bibliography.bib Revision:1.4394

  author =       "Federica Sarro and Filomena Ferrucci and 
                 Carmine Gravino",
  title =        "Single and Multi Objective Genetic Programming for
                 software development effort estimation",
  booktitle =    "Proceedings of the 27th Annual ACM Symposium on
                 Applied Computing",
  year =         "2012",
  editor =       "Sascha Ossowski and Paola Lecca",
  pages =        "1221--1226",
  address =      "Trento, Italy",
  publisher =    "ACM",
  keywords =     "genetic algorithms, genetic programming, SBSE, effort
                 estimation, empirical study, multi objective search",
  isbn13 =       "978-1-4503-0857-1",
  URL =          "",
  DOI =          "doi:10.1145/2245276.2231968",
  abstract =     "The idea of exploiting Genetic Programming (GP) to
                 estimate software development effort is based on the
                 observation that the effort estimation problem can be
                 formulated as an optimisation problem. Indeed, among
                 the possible models, we have to identify the one
                 providing the most accurate estimates. To this end a
                 suitable measure to evaluate and compare different
                 models is needed. However, in the context of effort
                 estimation there does not exist a unique measure that
                 allows us to compare different models but several
                 different criteria (e.g., MMRE, Pred(25), MdMRE) have
                 been proposed. Aiming at getting an insight on the
                 effects of using different measures as fitness
                 function, in this paper we analysed the performance of
                 GP using each of the five most used evaluation
                 criteria. Moreover, we designed a Multi-Objective
                 Genetic Programming (MOGP) based on Pareto optimality
                 to simultaneously optimise the five evaluation measures
                 and analysed whether MOGP is able to build estimation
                 models more accurate than those obtained using GP. The
                 results of the empirical analysis, carried out using
                 three publicly available datasets, showed that the
                 choice of the fitness function significantly affects
                 the estimation accuracy of the models built with GP and
                 the use of some fitness functions allowed GP to get
                 estimation accuracy comparable with the ones provided
                 by MOGP.",
  acmid =        "2231968",
  bibdate =      "2012-06-08",
  bibsource =    "DBLP,

Genetic Programming entries for Federica Sarro Filomena Ferrucci Carmine Gravino