A novel tree differential evolution using inter-symbol distance

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

  author =       "Jun-ichi Kushida and Akira Hara and 
                 Tetsuyuki Takahama",
  booktitle =    "7th IEEE International Workshop on Computational
                 Intelligence and Applications (IWCIA 2014)",
  title =        "A novel tree differential evolution using inter-symbol
  year =         "2014",
  month =        nov,
  pages =        "107--112",
  abstract =     "Differential Evolution (DE) is one of the evolutionary
                 algorithm that was developed to handle optimisation
                 problems over continuous domains. It's a
                 population-based stochastic search technique with
                 simple concept and high efficient. In recent year, many
                 DE variants were proposed and have been applied for
                 solving various problems. In addition, some DE based
                 techniques are modified to handle discrete optimisation
                 problems. One of them, Tree based DE (TreeDE), which
                 maps full trees to vectors and represents discrete
                 symbols by points in a real-valued vector space, is a
                 new DE-based tree discovering algorithm. TreeDE
                 directly can apply differential operation of DE to
                 individual vectors. However, since the search space of
                 genotypes in the TreeDE does not correspond to the
                 solution space of phenotypes (program tree), the
                 mutation operation will not always work effectively.
                 Therefore, we explicitly handle the distance of
                 programming tree and propose new TreeDE which optimises
                 tree structure based on DE. In the proposed method,
                 each individual has two types of genes: one express the
                 neighbourhood structure between the symbols, the other
                 represents a full tree structure of the program. By
                 evolving both genes simultaneously, effective mutation
                 operation and optimisation of the tree structure by DE
                 engine are realized. The proposed TreeDE is compared
                 with Genetic Programming (GP) on standard benchmark
                 problems, and experimental results showed the
                 effectiveness of the proposed TreeDE.",
  keywords =     "genetic algorithms, genetic programming, Differential
  DOI =          "doi:10.1109/IWCIA.2014.6988087",
  ISSN =         "1883-3977",
  notes =        "Also known as \cite{6988087}",

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