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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.",
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
Paypal/Ebay, San Jose, CA, USA",
Genetic Programming entries for Xin Li