Local Search is Underused in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  title =        "Local Search is Underused in Genetic Programming",
  author =       "Leonardo Trujillo and Emigdio Z-Flores and 
                 Perla S. {Juarez Smith} and Pierrick Legrand and Sara Silva and 
                 Mauro Castelli and Leonardo Vanneschi and 
                 Oliver Schuetze and Luiz Munoz",
  booktitle =    "Genetic Programming Theory and Practice XIV",
  year =         "2016",
  editor =       "William Tozier and Brian W. Goldman and 
                 Bill Worzel and Rick Riolo",
  series =       "Genetic and Evolutionary Computation",
  address =      "Ann Arbor, USA",
  month =        "19-21 " # may,
  publisher =    "Springer",
  note =         "Forthcoming",
  keywords =     "genetic algorithms, genetic programming, Local Search,
                 Bloat, NEAT",
  hal_id =       "hal-01388426",
  hal_version =  "v1",
  isbn13 =       "978-3-319-97087-5",
  URL =          "https://hal.inria.fr/hal-01388426",
  URL =          "https://www.researchgate.net/publication/312016495_Local_Search_is_Underused_in_Genetic_Programming",
  URL =          "https://www.springer.com/us/book/9783319970875",
  size =         "18 pages",
  abstract =     "There are two important limitations of standard
                 tree-based genetic programming (GP). First, GP tends to
                 evolve unnecessarily large programs, what is referred
                 to as bloat. Second, GP uses inefficient search
                 operators that focus on modifying program syntax. The
                 first problem has been studied in many works, with many
                 bloat control proposals. Regarding the second problem,
                 one approach is to use alternative search operators,
                 for instance geometric semantic operators, to improve
                 convergence. In this work, our goal is to
                 experimentally show that both problems can be
                 effectively addressed by incorporating a local search
                 optimizer as an additional search operator. Using
                 real-world problems, we show that this rather simple
                 strategy can improve the convergence and performance of
                 tree-based GP, while reducing program size. Given these
                 results, a question arises: why are local search
                 strategies so uncommon in GP? A small survey of popular
                 GP libraries suggests to us that local search is
                 underused in GP systems. We conclude by outlining
                 plausible answers for this question and highlighting
                 future work.",
  notes =        "also known as \cite{leonardo:hal-01388426}

                 Instituto Tecnologico de Tijuana, Mexico

                 Part of \cite{Tozier:2016:GPTP} to be published after
                 the workshop",

Genetic Programming entries for Leonardo Trujillo Emigdio Z-Flores Perla Sarahi Juarez-Smith Pierrick Legrand Sara Silva Mauro Castelli Leonardo Vanneschi Oliver Schuetze Luis Munoz Delgado