Computer implemented machine learning method and system

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Misc{nordin:2000:patent,
  author =       "Peter Nordin and Wolfgang Banzhaf",
  title =        "Computer implemented machine learning method and
                 system",
  howpublished = "U.S. Patent 6128607",
  year =         "2000",
  month =        "3 " # oct,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://patft.uspto.gov/netacgi/nph-Parser?Sect2=PTO1&Sect2=HITOFF&p=1&u=/netahtml/PTO/search-bool.html&r=1&f=G&l=50&d=PALL&RefSrch=yes&Query=PN/6128607",
  abstract =     "One or more machine code entities such as functions
                 are created which represent solutions to a problem and
                 are directly executable by a computer. The programs are
                 created and altered by a program in a higher level
                 language such as 'C' which is not directly executable,
                 but requires translation into executable machine code
                 through compilation, interpretation, translation, etc.
                 The entities are initially created as an integer array
                 that can be altered by the program as data, and are
                 executed by the program by recasting a pointer to the
                 array as a function type. The entities are evaluated by
                 executing them with training data as inputs, and
                 calculating fitnesses based on a predetermined
                 criterion. The entities are then altered based on their
                 fitnesses using a machine learning algorithm by
                 recasting the pointer to the array as a data (e.g.
                 integer) type. This process is iteratively repeated
                 until an end criterion is reached. The entities evolve
                 in such a manner as to improve their fitness, and one
                 entity is ultimately produced which represents an
                 optimal solution to the problem. Each entity includes a
                 plurality of directly executable machine code
                 instructions, a header, a footer, and a return
                 instruction. The instructions include branch
                 instructions which enable subroutines, leaf functions,
                 external function calls, recursion, and loops. The
                 system can be implemented on an integrated circuit
                 chip, with the entities stored in high speed memory in
                 a central processing unit.",
  notes =        "6,128,607",
}

Genetic Programming entries for Peter Nordin Wolfgang Banzhaf

Citations