Hybrid Single Node Genetic Programming for Symbolic Regression

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

  author =       "Jiri Kubalik and Eduard Alibekov and Jan Zegklitz and 
                 Robert Babuska",
  title =        "Hybrid Single Node Genetic Programming for Symbolic
  journal =      "Trans. Computational Collective Intelligence",
  bibdate =      "2017-05-28",
  bibsource =    "DBLP,
  booktitle =    "Transactions on Computational Collective Intelligence
  publisher =    "Springer",
  year =         "2016",
  volume =       "9770",
  editor =       "Ngoc Thanh Nguyen and Ryszard Kowalczyk and 
                 Joaquim Filipe",
  isbn13 =       "978-3-662-53524-0",
  pages =        "61--82",
  series =       "Lecture Notes in Computer Science",
  keywords =     "genetic algorithms, genetic programming, single node
                 genetic programming, symbolic regression",
  DOI =          "doi:10.1007/978-3-662-53525-7_4",
  abstract =     "This paper presents a first step of our research on
                 designing an effective and efficient GP-based method
                 for symbolic regression. First, we propose three
                 extensions of the standard Single Node GP, namely (1) a
                 selection strategy for choosing nodes to be mutated
                 based on depth and performance of the nodes, (2)
                 operators for placing a compact version of the
                 best-performing graph to the beginning and to the end
                 of the population, respectively, and (3) a local search
                 strategy with multiple mutations applied in each
                 iteration. All the proposed modifications have been
                 experimentally evaluated on five symbolic regression
                 benchmarks and compared with standard GP and SNGP. The
                 achieved results are promising showing the potential of
                 the proposed modifications to improve the performance
                 of the SNGP algorithm. We then propose two variants of
                 hybrid SNGP using a linear regression technique, LASSO,
                 to improve its performance. The proposed algorithms
                 have been compared to the state-of-the-art symbolic
                 regression methods that also make use of the linear
                 regression techniques on four real-world benchmarks.
                 The results show the hybrid SNGP algorithms are at
                 least competitive with or better than the compared

Genetic Programming entries for Jiri Kubalik Eduard Alibekov Jan Zegklitz Robert Babuska