A parallel and distributed semantic Genetic Programming system

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{vanneschi:2017:CECa,
  author =       "Leonardo Vanneschi and Bernardo Galvao",
  booktitle =    "2017 IEEE Congress on Evolutionary Computation (CEC)",
  title =        "A parallel and distributed semantic Genetic
                 Programming system",
  year =         "2017",
  editor =       "Jose A. Lozano",
  pages =        "121--128",
  address =      "Donostia, San Sebastian, Spain",
  publisher =    "IEEE",
  isbn13 =       "978-1-5090-4601-0",
  abstract =     "In the last few years, geometric semantic genetic
                 programming has incremented its popularity, obtaining
                 interesting results on several real life applications.
                 Nevertheless, the large size of the solutions generated
                 by geometric semantic genetic programming is still an
                 issue, in particular for those applications in which
                 reading and interpreting the final solution is
                 desirable. In this paper, we introduce a new parallel
                 and distributed genetic programming system, with the
                 objective of mitigating this drawback. The proposed
                 system (called MPHGP, which stands for Multi-Population
                 Hybrid Genetic Programming) is composed by two
                 subpopulations, one of which runs geometric semantic
                 genetic programming, while the other runs a standard
                 multi-objective genetic programming algorithm that
                 optimizes, at the same time, training error and the
                 size of the solutions. The two subpopulations evolve
                 independently and in parallel, exchanging individuals
                 at prefixed synchronization instants. The presented
                 experimental results, obtained on five real-life
                 symbolic regression applications, suggest that MPHGP is
                 able to find solutions that are comparable, or even
                 better, than the ones found by geometric semantic
                 genetic programming, both on training and on unseen
                 testing data. At the same time, MPHGP is also able to
                 find solutions that are significantly smaller than the
                 ones found by geometric semantic genetic programming.",
  keywords =     "genetic algorithms, genetic programming, algorithm
                 theory, geometry, MPHGP, distributed semantic genetic
                 programming system, geometric semantic genetic
                 programming, multiobjective genetic programming
                 algorithm, multipopulation hybrid genetic programming,
                 prefixed synchronization instants, symbolic regression
                 applications, Optimization, Semantics, Sociology,
                 Standards, Statistics, Training",
  isbn13 =       "978-1-5090-4601-0",
  DOI =          "doi:10.1109/CEC.2017.7969304",
  month =        "5-8 " # jun,
  notes =        "IEEE Catalog Number: CFP17ICE-ART Also known as
                 \cite{7969304}",
}

Genetic Programming entries for Leonardo Vanneschi Bernardo Galvao

Citations