Semantic Forward Propagation for Symbolic Regression

Created by W.Langdon from gp-bibliography.bib Revision:1.4333

  author =       "Marcin Szubert and Anuradha Kodali and 
                 Sangram Ganguly and Kamalika Das and Josh C. Bongard",
  title =        "Semantic Forward Propagation for Symbolic Regression",
  booktitle =    "14th International Conference on Parallel Problem
                 Solving from Nature",
  year =         "2016",
  editor =       "Julia Handl and Emma Hart and Peter R. Lewis and 
                 Manuel Lopez-Ibanez and Gabriela Ochoa and 
                 Ben Paechter",
  volume =       "9921",
  series =       "LNCS",
  pages =        "364--374",
  address =      "Edinburgh",
  month =        "17-21 " # sep,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, Program
                 semantics, Semantic backpropagation, Problem
                 decomposition, Symbolic regression",
  isbn13 =       "978-3-319-45823-6",
  DOI =          "doi:10.1007/978-3-319-45823-6_34",
  size =         "11 pages",
  abstract =     "In recent years, a number of methods have been
                 proposed that attempt to improve the performance of
                 genetic programming by exploiting information about
                 program semantics. One of the most important
                 developments in this area is semantic backpropagation.
                 The key idea of this method is to decompose a program
                 into two parts: a sub program and a context, and
                 calculate the desired semantics of the subprogram that
                 would make the entire program correct, assuming that
                 the context remains unchanged. In this paper we
                 introduce Forward Propagation Mutation, a novel
                 operator that relies on the opposite assumption.
                 instead of preserving the context, it retains the
                 subprogram and attempts to place it in the semantically
                 right context. We empirically compare the performance
                 of semantic backpropagation and forward propagation
                 operators on a set of symbolic regression benchmarks.
                 The experimental results demonstrate that semantic
                 forward propagation produces smaller programs that
                 achieve significantly higher generalization
  notes =        "PPSN2016",

Genetic Programming entries for Marcin Szubert Anuradha Kodali Sangram Ganguly Kamalika Das Josh C Bongard