Genetic Programming Exploratory Power and the Discovery of Functions

Created by W.Langdon from gp-bibliography.bib Revision:1.2031

@InProceedings{Rosca:1995:GPexpdf,
  author =       "Justinian P. Rosca",
  title =        "Genetic Programming Exploratory Power and the
                 Discovery of Functions",
  booktitle =    "Evolutionary Programming {IV} Proceedings of the
                 Fourth Annual Conference on Evolutionary Programming",
  year =         "1995",
  editor =       "John Robert McDonnell and Robert G. Reynolds and 
                 David B. Fogel",
  pages =        "719--736",
  address =      "San Diego, CA, USA",
  month =        "1-3 " # mar,
  publisher =    "MIT Press",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-13317-2",
  URL =          "ftp://ftp.cs.rochester.edu/pub/u/rosca/gp/95.ep.exploratory.ps.gz",
  size =         "18 pages",
  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, an analysis
                 of the causality of the crossover operator 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 =        "EP-95

                 Netscape v1.1 barfs on the url but ftp seems
                 ok.

                 Compares his own adaptive representation GP and Koza's
                 ADFs (together called hierarchical GP) with GP without
                 them using parity functions as the test case. Claims
                 evidence for {"}bottom up evolution thesis{"} ie later
                 in the (successfull) evolutionary process changes are
                 made higher up the function calling hierarchy and they
                 have small rather than dramatic effects.",
}

Genetic Programming entries for Justinian Rosca