- author = "Xin Li",
- title = "Self-emergence of structures in gene expression programming",
- school = "University of Illinois at Chicago",
- year = "2006",
- address = "USA",
- month = may # " 5",
- keywords = "genetic algorithms, genetic programming, gene expression programming",
-
URL = "
http://search.proquest.com/docview/304947984",
- size = "133 pages",
-
abstract = "Data mining tasks are pivotal for the improvement of
manufacturing and design processes. However, some of
the hidden patterns or relationships among the data are
very complex, which cannot be easily detected by
traditional data mining techniques. Several example
industrial applications include cell phone reliability
drop testing, call failure detection, wave filter
design, and simulations, etc. Gene Expression
Programming (GEP) was recently developed to address
this challenge in data analysis and knowledge
discovery. Being an evolutionary computation method,
GEP distinguishes itself by searching the global
optimum through a population of candidate solutions in
parallel and being able to produce solutions of any
possible form with minimum requirements of
pre-knowledge.
Although quite flexible, the algorithm still has limited performance with respect to complex problems since structure related information about evolving solutions is overlooked during its execution. This research aims to improve the problem solving ability of the GEP algorithm for complex data mining tasks by preserving and using the self-emergence of structures during its evolutionary process.
An incremental approach has been pursued to achieve the proposed research goal, including the investigation of the constant creation methods in GEP, for identifying and promoting good solution structures; the design of a new genotype representation, namely, Prefix Gene Expression Programming (P-GEP), for establishing a solution structure preserving evolutionary process; and the introduction and implementation of self-emergent structures in P-GEP, for speeding up the learning process by reusing some evolved useful structural components and hence decomposing the complexity of the target solutions.
Benchmark testing and theoretical analysis have both demonstrated that this line of work successfully assists the evolutionary process in advocating solutions with good functional structures, and finding meaningful building blocks to hierarchically form the final solutions following a faster fitness convergence curve, especially when applied to structurally complex problems. In general, more accurate solutions, higher success rates, and more compact solution structures have been achieved compared to the original GEP algorithm and other traditional methods.",
-
notes = "OCLC Number:
82267944
http://en.scientificcommons.org/xin_li http://gradworks.umi.com/32/33/3233164.html Adviser Peter C. Nelson School UNIVERSITY OF ILLINOIS AT CHICAGO Source DAI/B 67-09, p. , Dec 2006 Source Type Dissertation Subjects Computer science Publication Number 3233164
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