Grammatical Evolution for the Multi-Objective Integration and Test Order Problem

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@InProceedings{Mariani:2016:GECCO,
  author =       "Thaina Mariani and Giovani Guizzo and 
                 Silvia Regina Vergilio and Aurora Trinidad Ramirez Pozo",
  title =        "Grammatical Evolution for the Multi-Objective
                 Integration and Test Order Problem",
  booktitle =    "GECCO '16: Proceedings of the 2016 Annual Conference
                 on Genetic and Evolutionary Computation",
  year =         "2016",
  editor =       "Tobias Friedrich",
  pages =        "1069--1076",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution, SBSE, search based software engineering,
                 multi-objective, hyper-heuristic, evolutionary
                 algorithm",
  month =        "20-24 " # jul,
  organisation = "SIGEVO",
  address =      "Denver, USA",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  isbn13 =       "978-1-4503-4206-3",
  DOI =          "doi:10.1145/2908812.2908816",
  abstract =     "Search techniques have been successfully applied for
                 solving different software testing problems. However,
                 choosing, implementing and configuring a search
                 technique can be hard tasks. To reduce efforts spent in
                 such tasks, this paper presents an offline
                 hyper-heuristic named GEMOITO, based on Grammatical
                 Evolution (GE). The goal is to automatically generate a
                 Multi-Objective Evolutionary Algorithm (MOEA) to solve
                 the Integration and Test Order (ITO) problem. The MOEAs
                 are distinguished by components and parameters values,
                 described by a grammar. The proposed hyper-heuristic is
                 compared to conventional MOEAs and to a selection
                 hyper-heuristic used in related work. Results show that
                 GEMOITO can generate MOEAs that are statistically
                 better or equivalent to the compared algorithms.",
  notes =        "Federal University of Parana

                 GECCO-2016 A Recombination of the 25th International
                 Conference on Genetic Algorithms (ICGA-2016) and the
                 21st Annual Genetic Programming Conference (GP-2016)",
}

Genetic Programming entries for Thaina Mariani Giovani Guizzo Silvia Regina Vergilio Aurora Trinidad Ramirez Pozo

Citations