Hierarchical genetic algorithms operating on populations of computer programs

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "J. R. Koza",
  title =        "Hierarchical genetic algorithms operating on
                 populations of computer programs",
  editor =       "N. S. Sridharan",
  volume =       "1",
  pages =        "768--774",
  booktitle =    "Proceedings of the Eleventh International Joint
                 Conference on Artificial Intelligence IJCAI-89",
  year =         "1989",
  address =      "Detroit, MI, USA",
  keywords =     "genetic algorithms, genetic programming",
  publisher =    "Morgan Kaufmann",
  publisher_address = "San Mateo, CA, USA",
  month =        "20-25 " # aug,
  URL =          "http://www.genetic-programming.com/jkpdf/ijcai1989.pdf",
  URL =          "http://dl.acm.org/citation.cfm?id=1623755.1623877",
  acmid =        "1623877",
  abstract =     "Existing approaches to artificial intelligence
                 problems such as sequence induction, automatic
                 programming, machine learning, planning, and pattern
                 recognition typically require specification in advance
                 of the size and shape of the solution to the problem
                 (often in a unnatural and difficult way). This paper
                 reports on a new approach in which the size and shape
                 of the solution to such problems is dynamically created
                 using Darwinian principles of reproduction and survival
                 of the fittest. Moreover, the resulting solution is
                 inherently hierarchical. The paper describes computer
                 experiments, using the author's 4341 line LISP program,
                 in five areas of artificial intelligence, namely (1)
                 sequence induction (e.g. inducing a computational
                 procedure for the recursive Fibonacci sequence and
                 inducing a computational procedure for a cubic
                 polynomial sequence), (2) automatic programming (e.g.
                 discovering a computational procedure for solving pairs
                 of linear equations, solving quadratic equations for
                 complex roots, and discovering trigonometric
                 identities), (3) machine learning of functions (e.g.
                 learning a Boolean multiplexer function previously
                 studied in neural net and classifier system work and
                 learning the exclusive-or and parity function), (4)
                 planning (e.g. developing a robotic action sequence
                 that can stack an arbitrary initial configuration of
                 blocks into a specified order), and (5) pattern
                 recognition (e.g. translation-invariant recognition of
                 a simple one dimensional shape in a linear retina).",
  notes =        "


Genetic Programming entries for John Koza