Introducing Emergent Loose Modules into the Learning Process of a Linear Genetic Programming System

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Li:2006:ICMLA,
  author =       "Xin Li and Chi Zhou and Weimin Xiao and 
                 Peter C. Nelson",
  title =        "Introducing Emergent Loose Modules into the Learning
                 Process of a Linear Genetic Programming System",
  booktitle =    "5th International Conference on Machine Learning and
                 Applications, ICMLA '06",
  year =         "2006",
  pages =        "219--224",
  address =      "Orlando, USA",
  month =        dec,
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming",
  ISBN =         "0-7695-2735-3",
  DOI =          "doi:10.1109/ICMLA.2006.31",
  size =         "6 pages",
  abstract =     "Modularity and building blocks have drawn attention
                 from the genetic programming (GP) community for a long
                 time. The results are usually twofold: a hierarchical
                 evolution with adequate building block reuse can
                 accelerate the learning process, but rigidly defined
                 and excessively employed modules may also counteract
                 the expected advantages by confining the reachable
                 search space. In this work, we introduce the concept of
                 emergent loose modules based on a new linear GP system,
                 prefix gene expression programming (P-GEP), in an
                 attempt to balance between the stochastic exploration
                 and the hierarchical construction for the optimal
                 solutions. Emergent loose modules are dynamically
                 produced by the evolution, and are reusable as
                 sub-functions in later generations. The proposed
                 technique is fully illustrated with a simple symbolic
                 regression problem. The initial experimental results
                 suggest it is a flexible approach in identifying the
                 evolved regularity and the emergent loose modules are
                 critical in composing the best solutions",
  notes =        "fixed sized linear genome Dept. of Comput. Sci.,
                 Illinois Univ., Chicago, IL",
}

Genetic Programming entries for Xin Li Chi Zhou Weimin Xiao Peter C Nelson

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