Geometric Semantic Genetic Programming with Local Search

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

  author =       "Mauro Castelli and Leonardo Trujillo and 
                 Leonardo Vanneschi and Sara Silva and Emigdio Z-Flores and 
                 Pierrick Legrand",
  title =        "Geometric Semantic Genetic Programming with Local
  booktitle =    "GECCO '15: Proceedings of the 2015 Annual Conference
                 on Genetic and Evolutionary Computation",
  year =         "2015",
  editor =       "Sara Silva and Anna I Esparcia-Alcazar and 
                 Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and 
                 Christine Zarges and Luis Correia and Terence Soule and 
                 Mario Giacobini and Ryan Urbanowicz and 
                 Youhei Akimoto and Tobias Glasmachers and 
                 Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and 
                 Marta Soto and Carlos Cotta and Francisco B. Pereira and 
                 Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and 
                 Heike Trautmann and Jean-Baptiste Mouret and 
                 Sebastian Risi and Ernesto Costa and Oliver Schuetze and 
                 Krzysztof Krawiec and Alberto Moraglio and 
                 Julian F. Miller and Pawel Widera and Stefano Cagnoni and 
                 JJ Merelo and Emma Hart and Leonardo Trujillo and 
                 Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and 
                 Carola Doerr",
  isbn13 =       "978-1-4503-3472-3",
  pages =        "999--1006",
  keywords =     "genetic algorithms, genetic programming",
  month =        "11-15 " # jul,
  organisation = "SIGEVO",
  address =      "Madrid, Spain",
  URL =          "",
  DOI =          "doi:10.1145/2739480.2754795",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Since its introduction, Geometric Semantic Genetic
                 Programming (GSGP) has aroused the interest of numerous
                 researchers and several studies have demonstrated that
                 GSGP is able to effectively optimize training data by
                 means of small variation steps, that also have the
                 effect of limiting overfitting. In order to speed up
                 the search process, in this paper we propose a system
                 that integrates a local search strategy into GSGP
                 (called GSGP-LS). Furthermore, we present a hybrid
                 approach, that combines GSGP and GSGP-LS, aimed at
                 exploiting both the optimization speed of GSGP-LS and
                 the ability to limit overfitting of GSGP. The
                 experimental results we present, performed on a set of
                 complex real-life applications, show that GSGP-LS
                 achieves the best training fitness while converging
                 very quickly, but severely overfits. On the other hand,
                 GSGP converges slowly relative to the other methods,
                 but is basically not affected by overfitting. The best
                 overall results were achieved with the hybrid approach,
                 allowing the search to converge quickly, while also
                 exhibiting a noteworthy ability to limit overfitting.
                 These results are encouraging, and suggest that future
                 GSGP algorithms should focus on finding the correct
                 balance between the greedy optimization of a local
                 search strategy and the more robust geometric semantic
  notes =        "Also known as \cite{2754795} GECCO-2015 A joint
                 meeting of the twenty fourth international conference
                 on genetic algorithms (ICGA-2015) and the twentith
                 annual genetic programming conference (GP-2015)",

Genetic Programming entries for Mauro Castelli Leonardo Trujillo Leonardo Vanneschi Sara Silva Emigdio Z-Flores Pierrick Legrand