Direct Evolution of Hierarchical Solutions with Self-Emergent Substructures

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "Xin Li and Chi Zhou and Weimin Xiao and 
                 Peter C. Nelson",
  title =        "Direct Evolution of Hierarchical Solutions with
                 Self-Emergent Substructures",
  booktitle =    "The Fourth International Conference on Machine
                 Learning and Applications (ICMLA'05)",
  year =         "2005",
  pages =        "337--342",
  address =      "Los Angeles, California",
  month =        dec # " 15-17",
  publisher =    "IEEE press",
  keywords =     "genetic algorithms, genetic programming, Prefix Gene
                 Expression Programming",
  URL =          "",
  abstract =     "Linear genotype representation and modularity have
                 continuously received extensive attention from the
                 Genetic Programming (GP) community. The advantages of a
                 linear genotype include a convenient and efficient
                 implementation scheme. However, most existing
                 techniques using a linear genotype follow the
                 imperative programming language paradigm and a direct
                 hierarchical composition for the functionality of the
                 solution is under achieved. Our work is based on Prefix
                 Gene Expression Programming (P-GEP), a new GP method
                 featured by a prefix notation based linear genotype
                 representation. Since P-GEP uses a functional language
                 paradigm, its framework results in natural self
                 emergence of substructures as functional components
                 during the evolution. We propose to preserve and use
                 potentially useful emergent substructures via a dynamic
                 substructure library, empowering the algorithm to focus
                 the search on a higher level of the solution structure.
                 Preliminary experiments on the benchmark regression
                 problems have shown the effectiveness of this
  notes =        "cited by \cite{Spector:2011:GECCO}


Genetic Programming entries for Xin Li Chi Zhou Weimin Xiao Peter C Nelson