Title page for etd-0317114-105921


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URN etd-0317114-105921
Author Yu-Hsun Liao
Author's Email Address No Public.
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Department Computer Science and Engineering
Year 2013
Semester 2
Degree Master
Type of Document
Language English
Title A Quantum-inspired Evolutionary Clustering Algorithm
Date of Defense 2014-03-28
Page Count 102
Keyword
  • clustering
  • quantum-inspired evolutionary algorithm
  • Evolutionary algorithm
  • Abstract In recent years, a lot of evolutionary computation methods have been proposed to solve the combinatorial optimization problem based on the concepts of quantum mechanics. Although some of them are purposely presented for solving the data clustering problem, they are all far from optimal quality-wise. As such, this thesis proposes a new method, called quantum-inspired evolutionary clustering algorithm (QECA), to address the data clustering problem. The proposed method adds not only the concepts of clustering and the k-means to the traditional quantum-inspired evolutionary algorithm to make it work for clustering but also an effective repair operator to improve the performance of the proposed method. Experimental results on real world data show that the proposed method provides a promising result than those obtained by QEA and genetic k-means algorithm.
    Advisory Committee
  • Tzung-Pei Hong - chair
  • Chun-Wei Tsai - co-chair
  • Ming-Chao Chiang - advisor
  • Files
  • etd-0317114-105921.pdf
  • Indicate in-campus at 99 year and off-campus access at 99 year.
    Date of Submission 2014-04-17

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