Mining Innovative Augmented Graph Grammars for Argument Diagrams through Novelty Selection

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

  author =       "Linting Xue and Collin F. Lynch and Min Chi",
  title =        "Mining Innovative Augmented Graph Grammars for
                 Argument Diagrams through Novelty Selection",
  booktitle =    "Proceedings of the 10th International Conference on
                 Educational Data Mining (EDM 2017)",
  year =         "2017",
  editor =       "Xiangen Hu and Tiffany Barnes and 
                 Arnon Hershkovitz and Luc Paquette",
  pages =        "296--301",
  address =      "Wuhan China",
  month =        "25-28 " # jun,
  publisher =    "International Educational Data Mining Society",
  keywords =     "genetic algorithms, genetic programming, Heterogeneous
                 Rules, Augmented Graph Grammars, Argument Diagrams,
                 Evolutionary Computation, Novelty selection",
  URL =          "",
  size =         "6 pages",
  abstract =     "Augmented Graph Grammars are a graph-based rule
                 formalism that supports rich relational structures.
                 They can be used to represent complex social networks,
                 chemical structures, and student-produced argument
                 diagrams for automated analysis or grading. In prior
                 work we have shown that Evolutionary Computation (EC)
                 can be applied to induce empirically-valid grammars for
                 student-produced argument diagrams based upon fitness
                 selection. However this research has shown that while
                 the traditional EC algorithm does converge to an
                 optimal fitness, premature convergence can lead to it
                 getting stuck in local maxima, which may lead to
                 undiscovered rules. In this work, we augmented the
                 standard EC algorithm to induce more heterogeneous
                 Augmented Graph Grammars by replacing the fitness
                 selection with a novelty-based selection mechanism
                 every ten generations. Our results show that this
                 novelty selection increases the diversity of the
                 population and produces better, and more heterogeneous,
  cv-category =  "Peer-Reviewed Conference Paper",
  notes =        "",

Genetic Programming entries for Linting Xue Collin Lynch Min Chi