Destructiveness of Lexicographic Parsimony Pressure and Alleviation by a Concatenation Crossover in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

@InProceedings{Koetzing:2018:PPSN,
  author =       "Timo Koetzing and J. A. Gregor Lagodzinski and 
                 Johannes Lengler and Anna Melnichenko",
  title =        "Destructiveness of Lexicographic Parsimony Pressure
                 and Alleviation by a Concatenation Crossover in Genetic
                 Programming",
  booktitle =    "15th International Conference on Parallel Problem
                 Solving from Nature",
  year =         "2018",
  editor =       "Anne Auger and Carlos M. Fonseca and Nuno Lourenco and 
                 Penousal Machado and Luis Paquete and Darrell Whitley",
  volume =       "11102",
  series =       "LNCS",
  pages =        "42--54",
  address =      "Coimbra, Portugal",
  month =        "8-12 " # sep,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-319-99258-7",
  URL =          "https://www.springer.com/gp/book/9783319992587",
  DOI =          "doi:10.1007/978-3-319-99259-4_4",
  size =         "13 pages",
  abstract =     "For theoretical analyses there are two specifics
                 distinguishing GP from many other areas of evolutionary
                 computation. First, the variable size representations,
                 in particular yielding a possible bloat (i.e. the
                 growth of individuals with redundant parts). Second,
                 the role and realization of crossover, which is
                 particularly central in GP due to the tree-based
                 representation. Whereas some theoretical work on GP has
                 studied the effects of bloat, crossover had a
                 surprisingly little share in this work.

                 We analyse a simple crossover operator in combination
                 with local search, where a preference for small
                 solutions minimizes bloat (lexicographic parsimony
                 pressure); the resulting algorithm is denoted
                 Concatenation Crossover GP. For this purpose three
                 variants of the well-studied Majority test function
                 with large plateaus are considered. We show that the
                 Concatenation Crossover GP can efficiently optimize
                 these test functions, while local search cannot be
                 efficient for all three variants independent of
                 employing bloat control.",
  notes =        "PPSN2018 http://ppsn2018.dei.uc.pt

                 This two-volume set LNCS 11101 and 11102 constitutes
                 the refereed proceedings of the 15th International
                 Conference on Parallel Problem Solving from Nature,
                 PPSN 2018",
}

Genetic Programming entries for Timo Koetzing J A Gregor Lagodzinski Johannes Lengler Anna Melnichenko

Citations