Solving the exponential growth of symbolic regression trees in geometric semantic genetic programming

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

  author =       "Joao Francisco B. S. Martins and 
                 Luiz Otavio V. B. Oliveira and Luis F. Miranda and Felipe Casadei and 
                 Gisele L. Pappa",
  title =        "Solving the exponential growth of symbolic regression
                 trees in geometric semantic genetic programming",
  booktitle =    "GECCO '18: Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "2018",
  editor =       "Hernan Aguirre and Keiki Takadama and 
                 Hisashi Handa and Arnaud Liefooghe and Tomohiro Yoshikawa and 
                 Andrew M. Sutton and Satoshi Ono and Francisco Chicano and 
                 Shinichi Shirakawa and Zdenek Vasicek and 
                 Roderich Gross and Andries Engelbrecht and Emma Hart and 
                 Sebastian Risi and Ekart Aniko and Julian Togelius and 
                 Sebastien Verel and Christian Blum and Will Browne and 
                 Yusuke Nojima and Tea Tusar and Qingfu Zhang and 
                 Nikolaus Hansen and Jose Antonio Lozano and 
                 Dirk Thierens and Tian-Li Yu and Juergen Branke and 
                 Yaochu Jin and Sara Silva and Hitoshi Iba and 
                 Anna I Esparcia-Alcazar and Thomas Bartz-Beielstein and 
                 Federica Sarro and Giuliano Antoniol and Anne Auger and 
                 Per Kristian Lehre",
  isbn13 =       "978-1-4503-5618-3",
  pages =        "1151--1158",
  address =      "Kyoto, Japan",
  DOI =          "doi:10.1145/3205455.3205593",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        "15-19 " # jul,
  organisation = "SIGEVO",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "advances in Geometric Semantic Genetic Programming
                 (GSGP) have shown that this variant of Genetic
                 Programming (GP) reaches better results than its
                 predecessor for supervised machine learning problems,
                 particularly in the task of symbolic regression.
                 However, by construction, the geometric semantic
                 crossover operator generates individuals that grow
                 exponentially with the number of generations, resulting
                 in solutions with limited use. This paper presents a
                 new method for individual simplification named GSGP
                 with Reduced trees (GSGP-Red). GSGP-Red works by
                 expanding the functions generated by the geometric
                 semantic operators. The resulting expanded function is
                 guaranteed to be a linear combination that, in a second
                 step, has its repeated structures and respective
                 coefficients aggregated. Experiments in 12 real-world
                 datasets show that it is not only possible to create
                 smaller and completely equivalent individuals in
                 competitive computational time, but also to reduce the
                 number of nodes composing them by 58 orders of
                 magnitude, on average.",
  notes =        "Also known as \cite{3205593} GECCO-2018 A
                 Recombination of the 27th International Conference on
                 Genetic Algorithms (ICGA-2018) and the 23rd Annual
                 Genetic Programming Conference (GP-2018)",

Genetic Programming entries for Joao Francisco B S Martins Luiz Otavio Vilas Boas Oliveira Luis Fernando Miranda Felipe Casadei Gisele L Pappa