Efficient program generation by evolving graph structures with multi-start nodes

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@Article{Mabu20113618,
  author =       "Shingo Mabu and Kotaro Hirasawa",
  title =        "Efficient program generation by evolving graph
                 structures with multi-start nodes",
  journal =      "Applied Soft Computing",
  volume =       "11",
  number =       "4",
  pages =        "3618--3624",
  year =         "2011",
  ISSN =         "1568-4946",
  DOI =          "doi:10.1016/j.asoc.2011.01.033",
  URL =          "http://www.sciencedirect.com/science/article/B6W86-5230PMW-2/2/83938061ebc19cc5a8ad1b3aa41d96c3",
  keywords =     "genetic algorithms, genetic programming, Evolutionary
                 computation, Program generation, Graph structure,
                 Even-n-Parity problem, Mirror Symmetry problem",
  abstract =     "Automatic program generation is one of the applicable
                 fields of evolutionary computation, and Genetic
                 Programming (GP) is the typical method for this field.
                 On the other hand, Genetic Network Programming (GNP)
                 has been proposed as an extended algorithm of GP in
                 terms of gene structures. GNP is a graph-based
                 evolutionary algorithm and applied to automatic program
                 generation in this paper. GNP has directed graph
                 structures which have some features inherently, for
                 example, re-usability of nodes and the small number of
                 nodes. These features contribute to creating
                 complicated programs with compact structures and never
                 cause bloat. In this paper, the extended algorithm of
                 GNP is proposed, which can create plural programs
                 simultaneously in one individual by using multi-start
                 nodes. In addition, GNP can evolve the programs in one
                 individual considering the fitness and also its
                 standard deviation in order to evolve the plural
                 programs efficiently. In the simulations, Even-n-Parity
                 problem and Mirror Symmetry problem are used for the
                 performance evaluation, and the results show that the
                 proposed method outperforms the standard GNP with
                 single start node.",
}

Genetic Programming entries for Shingo Mabu Kotaro Hirasawa

Citations