Genetic Evolution of Machine Language Software

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

@InProceedings{crepeau:1995:GEMS,
  author =       "Ronald L. Crepeau",
  title =        "Genetic Evolution of Machine Language Software",
  booktitle =    "Proceedings of the Workshop on Genetic Programming:
                 From Theory to Real-World Applications",
  year =         "1995",
  editor =       "Justinian P. Rosca",
  pages =        "121--134",
  address =      "Tahoe City, California, USA",
  month =        "9 " # jul,
  keywords =     "genetic algorithms, genetic programming, memory",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/GEMS_Article.pdf",
  size =         "14 pages",
  abstract =     "Genetic Programming (GP) has a proven capability to
                 routinely evolve software that provides a solution
                 function for the specified problem. Prior work in this
                 area has been based upon the use of relatively small
                 sets of pre-defined operators and terminals germane to
                 the problem domain. This paper reports on GP
                 experiments involving a large set of general purpose
                 operators and terminals. Specifically, a microprocessor
                 architecture with 660 instructions and 255 bytes of
                 memory provides the operators and terminals for a GP
                 environment. Using this environment, GP is applied to
                 the beginning programmer problem of generating a
                 desired string output, e.g., {"}Hello World{"}. Results
                 are presented on: the feasibility of using this large
                 operator set and architectural representation; and, the
                 computations required to breed string outputting
                 programs vs. the size of the string and the GP
                 parameters employed.",
  notes =        "Z80 Machine code evolved to write {"}Hello World{"}
                 HWP 660 instructions and 255 byte RAM (modular
                 arithmetic used to address indexed memory)

                 GEMS genetic evolution of machine language software
                 Breeding system similar to crowding and Tackett's
                 Softbrood selection (max litter size of 12). GA like
                 crossover acts on code and contents of memory. Pool of
                 1500 member 0.20 mutation rate.

                 {"}indicates that the problem difficulty, over the
                 range of the test and in terms of required spawns,
                 while increasing rapidly, does not appear to be
                 combinatorial or exponential{"} (suggests O(n**3)
                 ).

                 Discussion of statistics of number of useful terminals
                 in random and later populations.

                 Memory initialised to random values. {"}Cultural
                 memory{"} cf \cite{spector:1996:ctiGP}.

                 Steady state GA. 2 types of Mutation (20 percent).
                 While JP jump and subroutines are discussed the problem
                 does not need iteration to solve it.

                 part of \cite{rosca:1995:ml}",
}

Genetic Programming entries for Ronald L Crepeau