PSXO: Population-wide Semantic Crossover

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

@InProceedings{Vanneschi:2017:GECCO,
  author =       "Leonardo Vanneschi and Mauro Castelli and 
                 Luca Manzoni and Krzysztof Krawiec and Alberto Moraglio and 
                 Sara Silva and Ivo Goncalves",
  title =        "PSXO: Population-wide Semantic Crossover",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference Companion",
  series =       "GECCO '17",
  year =         "2017",
  isbn13 =       "978-1-4503-4939-0",
  address =      "Berlin, Germany",
  pages =        "257--258",
  size =         "2 pages",
  URL =          "http://doi.acm.org/10.1145/3067695.3076003",
  DOI =          "doi:10.1145/3067695.3076003",
  acmid =        "3076003",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  keywords =     "genetic algorithms, genetic programming, inverse
                 matrix, population-wide crossover, real-life problems,
                 semantics",
  month =        "15-19 " # jul,
  abstract =     "Since its introduction, Geometric Semantic Genetic
                 Programming (GSGP) has been the inspiration to ideas on
                 how to reach optimal solutions efficiently. Among
                 these, in 2016 Pawlak has shown how to analytically
                 construct optimal programs by means of a linear
                 combination of a set of random programs. Given the
                 simplicity and excellent results of this method (LC)
                 when compared to GSGP, the author concluded that GSGP
                 is overkill. However, LC has limitations, and it was
                 tested only on simple benchmarks. In this paper, we
                 introduce a new method, Population-Wide Semantic
                 Crossover (PSXO), also based on linear combinations of
                 random programs, that overcomes these limitations. We
                 test the first variant (Inv) on a diverse set of
                 complex real-life problems, comparing it to LC, GSGP
                 and standard GP. We realize that, on the studied
                 problems, both LC and Inv are outperformed by GSGP, and
                 sometimes also by standard GP. This leads us to the
                 conclusion that GSGP is not overkill. We also introduce
                 a second variant (GPinv) that integrates evolution with
                 the approximation of optimal programs by means of
                 linear combinations. GPinv outperforms both LC and Inv
                 on unseen test data for the studied problems.",
  notes =        "Also known as
                 \cite{Vanneschi:2017:PPS:3067695.3076003},
                 \cite{vanneschi2017psxo} GECCO-2017 A Recombination of
                 the 26th International Conference on Genetic Algorithms
                 (ICGA-2017) and the 22nd Annual Genetic Programming
                 Conference (GP-2017)",
}

Genetic Programming entries for Leonardo Vanneschi Mauro Castelli Luca Manzoni Krzysztof Krawiec Alberto Moraglio Sara Silva Ivo Goncalves

Citations