Automatic python programming using stack-based genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Hyun soo Park and Kyung Joong Kim",
  title =        "Automatic python programming using stack-based genetic
  booktitle =    "GECCO 2012 Late breaking abstracts workshop",
  year =         "2012",
  editor =       "Katya Rodriguez and Christian Blum",
  isbn13 =       "978-1-4503-1178-6",
  keywords =     "genetic algorithms, genetic programming",
  pages =        "641--642",
  month =        "7-11 " # jul,
  organisation = "SIGEVO",
  address =      "Philadelphia, Pennsylvania, USA",
  DOI =          "doi:10.1145/2330784.2330899",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Traditional genetic programming uses tree-like data
                 structure to represent a program. It should be
                 converted into a Lisp code, or needs a custom-made
                 virtual machine or interpreter to execute the program
                 generated. Recently, there is a study on genetic
                 programming directly using Java bytecode, a practical
                 intermediate language. It evolves a series of commands
                 that manipulate stack and registers in the virtual
                 machine and represents them with a simple list data
                 structure instead of tree. Evolving the intermediate
                 language is promising because 1) it is easy to combine
                 an existing program with an automatically generated
                 program, 2) there are several available development
                 tools and environments for the language including
                 virtual machine, decompiler, optimizer and so on, and
                 3) incorporating the list data structure into the
                 evolutionary algorithm is simple and straightforward.
                 In this research, we propose to evolve bytecode of
                 Python programming language by stack-based genetic
                 programming. Python is a flexible and popular
                 programming language powered by plenty of research
                 tools. For the evolution, we developed representation
                 and genetic operations for the Python language. We
                 report that the proposed method produced successful
                 Python codes for two regression problems.",
  notes =        "Also known as \cite{2330899} Distributed at

                 ACM Order Number 910122.",

Genetic Programming entries for Hyun-Soo Park Kyung-Joong Kim