Hierarchical Self-Organization in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Justinian P. Rosca and Dana H. Ballard",
  title =        "Hierarchical Self-Organization in Genetic
  booktitle =    "Proceedings of the Eleventh International Conference
                 on Machine Learning",
  year =         "1994",
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://ftp.cs.rochester.edu/pub/u/rosca/gp/94.ml.hierarchical_so_gp.ps.gz",
  size =         "6 pages",
  abstract =     "This paper presents an approach to automatic discovery
                 of functions in Genetic Programming. The approach is
                 based on discovery of useful building blocks by
                 analyzing the evolution trace, generalizing blocks to
                 define new functions, and finally adapting the problem
                 representation on-the-fly. Adapting the representation
                 determines a hierarchical organization of the extended
                 function set which enables a restructuring of the
                 search space so that solutions can be found more
                 easily. Measures of complexity of solution trees are
                 defined for an adaptive representation framework. The
                 minimum description length principle is applied to
                 justify the feasibility of approaches based on a
                 hierarchy of discovered functions and to suggest
                 alternative ways of defining a problem's fitness
                 function. Preliminary empirical results are
  notes =        "


Genetic Programming entries for Justinian Rosca Dana H Ballard