Pattern Recognition via Machine Learning with Genetic Decision-Programming

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

@PhdThesis{oai:etd.ohiolink.edu:wright1133882117,
  title =        "Pattern Recognition via Machine Learning with Genetic
                 Decision-Programming",
  author =       "Carl C. Hoff",
  year =         "2005",
  school =       "Department of Computer Science and Engineering, Wright
                 State University",
  bibsource =    "OAI-PMH server at www.ohiolink.edu",
  language =     "English",
  oai =          "oai:etd.ohiolink.edu:wright1133882117",
  rights =       "unrestricted; Copyright information available at the
                 source archive",
  keywords =     "genetic algorithms, genetic programming, Computer
                 Science (0984), Pattern Recognition, Machine Learning,
                 Evolutionary Computation, Genetic
                 Decision-Programming",
  URL =          "http://rave.ohiolink.edu/etdc/view?acc_num=wright1133882117.pdf",
  URL =          "http://rave.ohiolink.edu/etdc/view?acc_num=wright1133882117",
  size =         "179 pages",
  abstract =     "In the intersection of pattern recognition, machine
                 learning, and evolutionary computation is a new search
                 technique by which computers might program themselves.
                 That technique is called genetic decision-programming.
                 A computer can gain the ability to distinguish among
                 the things that it needs to recognise by using genetic
                 decision-programming for pattern discovery and concept
                 learning. Those patterns and concepts can be easily
                 encoded in the spines of a decision program (tree or
                 diagram). A spine consists of two parts: (1) the
                 test-outcome pairs along a path from the program's root
                 to any of its leaves and (2) the conclusion in that
                 leaf. The test-outcome pairs specify a pattern and the
                 conclusion identifies the corresponding
                 concept.

                 Genetic decision-programming combines and extends
                 discrete decision theory with the principles of
                 genetics and natural selection. The resulting algorithm
                 searches for those decision programs that best satisfy
                 some user-defined criteria. Each program mates problem
                 decompositions with subproblem solutions, and consists
                 of overlapping spines. Those spines are manipulated by
                 three context-sensitive operators. The context defines
                 a subproblem and is determined by the operator's point
                 of application within a decision program.
                 Macro-mutation generates a new solution for that
                 context; mini-mutation restructures the existing
                 solution for that context; and spine crossover inserts
                 another program's solution for that context. Those
                 solutions are encoded in the spines. Thus the operators
                 recompose, restructure and recombine spines as the
                 search technique evolves a population of decision
                 programs to satisfy the user-defined criteria. Genetic
                 decision-programming overcomes the difficulties
                 encountered when evolving decision programs with
                 genetic programming techniques that rely on subtree
                 crossover. Those impractical techniques require too
                 much memory and computation. Subtree crossover
                 exchanges random subtrees of broken spines without
                 regard for context. Meaning is lost. In contrast, the
                 spine crossover of genetic decision-programming crosses
                 entire spines and uses them in context. Meaning is
                 retained. This means that genetic decision-programming
                 can be applied to practical problems. In an experiment,
                 it consistently gave very good results without the
                 variability from problem to problem of other more
                 conventional decision-tree construction techniques.",
}

Genetic Programming entries for Carl C Hoff

Citations