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wright1133882117.pdf (1.15 MB)
ETD Abstract Container
Abstract Header
Pattern Recognition via Machine Learning with Genetic Decision-Programming
Author Info
Hoff, Carl C.
Permalink:
http://rave.ohiolink.edu/etdc/view?acc_num=wright1133882117
Abstract Details
Year and Degree
2005, Doctor of Philosophy (PhD), Wright State University, Computer Science and Engineering PhD.
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 recognize 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 generatyes a new solution for that context; mini-mutation restructires 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.
Committee
Mateen Rizki (Advisor)
Pages
179 p.
Subject Headings
Computer Science
Keywords
Pattern Recognition
;
Machine Learning
;
Evolutionary Computation
;
Genetic Algorithm
;
Genetic Programming
;
Genetic Decision-Programming
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Citations
Hoff, C. C. (2005).
Pattern Recognition via Machine Learning with Genetic Decision-Programming
[Doctoral dissertation, Wright State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=wright1133882117
APA Style (7th edition)
Hoff, Carl.
Pattern Recognition via Machine Learning with Genetic Decision-Programming.
2005. Wright State University, Doctoral dissertation.
OhioLINK Electronic Theses and Dissertations Center
, http://rave.ohiolink.edu/etdc/view?acc_num=wright1133882117.
MLA Style (8th edition)
Hoff, Carl. "Pattern Recognition via Machine Learning with Genetic Decision-Programming." Doctoral dissertation, Wright State University, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=wright1133882117
Chicago Manual of Style (17th edition)
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Document number:
wright1133882117
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Copyright Info
© 2005, all rights reserved.
This open access ETD is published by Wright State University and OhioLINK.