Expressive genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Lee Spector",
  title =        "Expressive genetic programming",
  booktitle =    "GECCO '13 Companion: Proceeding of the fifteenth
                 annual conference companion on Genetic and evolutionary
                 computation conference companion",
  year =         "2013",
  editor =       "Christian Blum and Enrique Alba and 
                 Thomas Bartz-Beielstein and Daniele Loiacono and 
                 Francisco Luna and Joern Mehnen and Gabriela Ochoa and 
                 Mike Preuss and Emilia Tantar and Leonardo Vanneschi and 
                 Kent McClymont and Ed Keedwell and Emma Hart and 
                 Kevin Sim and Steven Gustafson and 
                 Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and 
                 Nikolaus Hansen and Olaf Mersmann and Petr Posik and 
                 Heike Trautmann and Muhammad Iqbal and Kamran Shafi and 
                 Ryan Urbanowicz and Stefan Wagner and 
                 Michael Affenzeller and David Walker and Richard Everson and 
                 Jonathan Fieldsend and Forrest Stonedahl and 
                 William Rand and Stephen L. Smith and Stefano Cagnoni and 
                 Robert M. Patton and Gisele L. Pappa and 
                 John Woodward and Jerry Swan and Krzysztof Krawiec and 
                 Alexandru-Adrian Tantar and Peter A. N. Bosman and 
                 Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and 
                 David L. Gonzalez-Alvarez and 
                 Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and 
                 Kenneth Holladay and Tea Tusar and Boris Naujoks",
  isbn13 =       "978-1-4503-1964-5",
  keywords =     "genetic algorithms, genetic programming",
  pages =        "683--714",
  month =        "6-10 " # jul,
  organisation = "SIGEVO",
  address =      "Amsterdam, The Netherlands",
  DOI =          "doi:10.1145/2464576.2480806",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "The language in which evolving programs are expressed
                 can have significant impacts on the problem-solving
                 capabilities of a genetic programming system. These
                 impacts stem both from the absolute computational power
                 of the languages that are used, as elucidated by formal
                 language theory, and from the ease with which various
                 computational structures can be produced by random code
                 generation and by the action of genetic operators.
                 Highly expressive languages can facilitate the
                 evolution of programs for any computable function
                 using, when appropriate, multiple data types, evolved
                 subroutines, evolved control structures, evolved data
                 structures, and evolved modular program and data
                 architectures. In some cases expressive languages can
                 even support the evolution of programs that express
                 methods for their own reproduction and variation (and
                 hence for the evolution of their offspring).

                 This tutorial will begin with a comparative survey of
                 approaches to the evolution of programs in expressive
                 programming languages ranging from machine code to
                 graphical and grammatical representations. Within this
                 context it will then provide a detailed introduction to
                 the Push programming language, which was designed
                 specifically for expressiveness and specifically for
                 use in genetic programming systems. Push programs are
                 syntactically unconstrained but can nonetheless make
                 use of multiple data types and express arbitrary
                 control structures, supporting the evolution of
                 complex, modular programs in a particularly simple and
                 flexible way. The Push language will be described and
                 ten years of Push-based research, including the
                 production of human-competitive results, will be
                 briefly surveyed. The tutorial will conclude with a
                 discussion of recent enhancements to Push that are
                 intended to support the evolution of complex and robust
                 software systems.",
  notes =        "Also known as \cite{2480806} Distributed at

Genetic Programming entries for Lee Spector