Metaheuristic optimization frameworks: a survey and benchmarking

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Jose Antonio Parejo and Antonio Ruiz-Cortes and 
                 Sebastian Lozano and Pablo Fernandez",
  title =        "Metaheuristic optimization frameworks: a survey and
  journal =      "Soft Computing",
  year =         "2012",
  volume =       "16",
  number =       "3",
  pages =        "527--561",
  keywords =     "genetic algorithms, genetic programming",
  publisher =    "Springer-Verlag",
  ISSN =         "1432-7643",
  URL =          "",
  DOI =          "doi:10.1007/s00500-011-0754-8",
  language =     "English",
  size =         "35 pages",
  abstract =     "This paper performs an unprecedented comparative study
                 of Metaheuristic optimisation frameworks. As criteria
                 for comparison a set of 271 features grouped in 30
                 characteristics and 6 areas has been selected. These
                 features include the different metaheuristic techniques
                 covered, mechanisms for solution encoding, constraint
                 handling, neighbourhood specification, hybridisation,
                 parallel and distributed computation, software
                 engineering best practices, documentation and user
                 interface, etc. A metric has been defined for each
                 feature so that the scores obtained by a framework are
                 averaged within each group of features, leading to a
                 final average score for each framework. Out of 33
                 frameworks ten have been selected from the literature
                 using well-defined filtering criteria, and the results
                 of the comparison are analysed with the aim of
                 identifying improvement areas and gaps in specific
                 frameworks and the whole set. Generally speaking, a
                 significant lack of support has been found for
                 hyper-heuristics, and parallel and distributed
                 computing capabilities. It is also desirable to have a
                 wider implementation of some Software Engineering best
                 practises. Finally, a wider support for some meta
                 heuristics and hybridisation capabilities is needed.",
  notes =        "EasyLocal (Di Gaspero and Schaerf 2003) 2.0

                 ECJ (Luke et al. 2009) 20

                 EO/ ParadisEO/ MOEO/ PEO (Cahon et al. 2004) 1.2

                 EvA2 (Kronfeld et al. 2010) 2

                 FOM (Parejo et al. 2003) 0.8

                 HeuristicLab (Wagner 2009) 3.3

                 JCLEC (and KEEL) (Ventura et al. 2008) 4.0

                 MALLBA (Alba et al. 2007) 2.0

                 Optimization Algorithm Toolkit (Brownlee 2007) 1.4

                 Opt4j (Martin Lukasiewycz and Helwig 2009) 2.1

Genetic Programming entries for Jose Antonio Parejo Antonio Ruiz-Cortes Sebastian Lozano Pablo Fernandez