Multi-Objective Gene Expression Programming for Clustering

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@Article{journals/itc/ZhengJC12,
  title =        "Multi-Objective Gene Expression Programming for
                 Clustering",
  author =       "Yifei Zheng and Lixin Jia and Hui Cao",
  journal =      "ITC",
  year =         "2012",
  number =       "3",
  volume =       "41",
  pages =        "283--294",
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming, Clustering, multi-objective,
                 evolutionary algorithm",
  bibdate =      "2014-01-30",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/itc/itc41.html#ZhengJC12",
  URL =          "http://dx.doi.org/10.5755/j01.itc.41.3.1330",
  URL =          "http://www.itc.ktu.lt/index.php/ITC/article/view/1330",
  DOI =          "doi:10.5755/j01.itc.41.3.1330",
  abstract =     "This paper proposes a multi-objective gene expression
                 programming for clustering (MGEPC), which could
                 automatically determine the number of clusters and the
                 appropriate partitioning from the data set. The
                 clustering algebraic operations of gene expression
                 programming are extended first. Then based on the
                 framework of the Non-dominated Sorting Genetic
                 Algorithm-II, two enhancements are proposed in MGEPC.
                 First, a multi-objective k-means clustering is proposed
                 for local search, where the total symmetrical
                 compactness and the cluster connectivity are used as
                 two complementary objectives and the point symmetry
                 based distance is adopted as the distance metric.
                 Second, the power-law distribution based selection
                 strategy is proposed for the parent population
                 generation. In addition, the external archive and the
                 archive truncation are used to keep a historical record
                 of the non-dominated solutions found along the search
                 process. Experiments are performed on five artificial
                 and three real-life data sets. Results show that the
                 proposed algorithm outperforms the PESA-II based
                 clustering method (MOCK), the archived multiobjective
                 simulated annealing based clustering technique with
                 point symmetry based distance (VAMOSA) and the
                 single-objective version of gene expression programming
                 based clustering technique (GEP-Cluster).",
}

Genetic Programming entries for Yifei Zheng Lixin Jia Hui Cao

Citations