An Analysis of Hierarchical Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@TechReport{Rosca:1995:aHGP,
  author =       "Justinian P. Rosca",
  title =        "An Analysis of Hierarchical Genetic Programming",
  institution =  "University of Rochester",
  address =      "Rochester, NY, USA",
  year =         "1995",
  type =         "Technical Report",
  number =       "566",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://ftp.cs.rochester.edu/pub/u/rosca/gp/95.tr566.ps.gz",
  abstract =     "Hierarchical genetic programming (HGP) approaches rely
                 on the discovery, modification, and use of new
                 functions to accelerate evolution. This paper provides
                 a qualitative explanation of the improved behavior of
                 HGP, based on an analysis of the evolution process from
                 the dual perspective of diversity and causality. From a
                 static point of view, the use of an HGP approach
                 enables the manipulation of a population of higher
                 diversity programs. Higher diversity increases the
                 exploratory ability of the genetic search process, as
                 demonstrated by theoretical and experimental fitness
                 distributions and expanded structural complexity of
                 individuals. From a dynamic point of view, this report
                 analyzes the causality of the crossover operator.
                 Causality relates changes in the structure of an object
                 with the effect of such changes, i.e. changes in the
                 properties or behavior of the object. The analyses of
                 crossover causality suggests that HGP discovers and
                 exploits useful structures in a bottom-up, hierarchical
                 manner. Diversity and causality are complementary,
                 affecting exploration and exploitation in genetic
                 search. Unlike other machine learning techniques that
                 need extra machinery to control the tradeoff between
                 them, HGP automatically trades off exploration and
                 exploitation.",
  notes =        "Some of the discussions in this report are summarized
                 in \cite{Rosca:1995:cause}",
}

Genetic Programming entries for Justinian Rosca

Citations