Geometric Semantic Genetic Programming Using External Division of Parents

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Akira Hara and Jun-Ichi Kushida and Kei Kisaka and 
                 Tetsuyuki Takahama",
  booktitle =    "4th IIAI International Congress on Advanced Applied
                 Informatics (IIAI-AAI)",
  title =        "Geometric Semantic Genetic Programming Using External
                 Division of Parents",
  year =         "2015",
  pages =        "189--194",
  abstract =     "In this paper, we focus on symbolic regression
                 problems, in which we find functions approximating the
                 relationships between given input and output data. If
                 we do not have the knowledge on the structure (e.g.
                 Degree) of the true functions, Genetic Programming (GP)
                 is often used for evolving tree structural numerical
                 expressions. In GP, crossover operator has a great
                 influence on the quality of the acquired solutions.
                 Therefore, various crossover operators have been
                 proposed. Recently, new crossover operators based on
                 semantics of tree structures have attracted many
                 attentions for efficient search. In the semantics-based
                 crossover, offspring is created from its parental
                 individuals so that the offspring can be similar to the
                 parents not structurally but semantically. Geometric
                 Semantic Genetic Programming (GSGP) is a method in
                 which offspring is produced by a convex combination of
                 two parental individuals. This operation corresponds to
                 the internal division of two parents. This method can
                 optimise solutions efficiently because the crossover
                 operator always produces better solution than a worse
                 parent. But, in GSGP, if the true function exists
                 outside of two parents in semantic space, it is
                 difficult to produce better solution than both of the
                 parents. In this paper, we propose an improved GSGP
                 which can also consider external divisions as well as
                 internal ones. By comparing the search performance
                 among several crossover operators in symbolic
                 regression problems, we showed that our methods are
                 superior to the standard GP and conventional GSGP.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/IIAI-AAI.2015.245",
  month =        jul,
  notes =        "Also known as \cite{7373899}",

Genetic Programming entries for Akira Hara Jun-ichi Kushida Kei Kisaka Tetsuyuki Takahama