Deterministic Geometric Semantic Genetic Programming with Optimal Mate Selection

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  author =       "A. Hara and J. i. Kushida and T. Takahama",
  booktitle =    "2016 IEEE International Conference on Systems, Man,
                 and Cybernetics (SMC)",
  title =        "Deterministic Geometric Semantic Genetic Programming
                 with Optimal Mate Selection",
  year =         "2016",
  pages =        "003387--003392",
  abstract =     "To solve symbolic regression problems, Genetic
                 Programming (GP) is often used for evolving tree
                 structural numerical expressions. Recently, new
                 crossover operators based on semantics of tree
                 structures have attracted many attentions. In the
                 semantics-based crossover, offspring is created from
                 its parental individuals so that the offspring can
                 inherit the characteristics of the parents not
                 structurally but semantically. Geometric Semantic GP
                 (GSGP) is a method in which offspring is produced by a
                 convex combination of two parental individuals. In
                 order to improve the search performance of GSGP,
                 deterministic Geometric Semantic Crossover using the
                 information of the target semantics has been proposed.
                 In conventional GSGP, ratios of convex combinations are
                 determined at random. On the other hand, the
                 deterministic crossover can use optimal ratios for
                 affine combinations of parental individuals so that
                 created offspring can be closest to the target
                 solution. In these methods, parents which crossover
                 operators will be applied to are selected based only on
                 their fitness. In this paper, we propose a new
                 selection method of parents for generating offspring
                 which can approach to a target solution more
                 efficiently. In this method, we select a pair of
                 parents so that a distance between a straight line
                 connecting the parents and a target point can be
                 smallest in semantic space. We confirmed that our
                 method showed better performance than conventional GSGP
                 in several symbolic regression problems.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/SMC.2016.7844757",
  month =        oct,
  notes =        "Also known as \cite{7844757}",

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