Self-emergence of structures in gene expression programming

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  author =       "Xin Li",
  title =        "Self-emergence of structures in gene expression
  school =       "University of Illinois at Chicago",
  year =         "2006",
  address =      "USA",
  month =        may # " 5",
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming",
  URL =          "",
  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

                 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:

                 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

                 Paypal/Ebay, San Jose, CA, USA",

Genetic Programming entries for Xin Li