Hierarchical Knowledge in Self-Improving Grammar Based Genetic Programming

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@InProceedings{Wong:2016:PPSN,
  author =       "Pak-Kan Wong and Man-Leung Wong and Kwong-Sak Leung",
  title =        "Hierarchical Knowledge in Self-Improving Grammar Based
                 Genetic Programming",
  booktitle =    "14th International Conference on Parallel Problem
                 Solving from Nature",
  year =         "2016",
  editor =       "Julia Handl and Emma Hart and Peter R. Lewis and 
                 Manuel Lopez-Ibanez and Gabriela Ochoa and 
                 Ben Paechter",
  volume =       "9921",
  series =       "LNCS",
  pages =        "270--280",
  address =      "Edinburgh",
  month =        "17-21 " # sep,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, Hierarchical
                 knowledge learning, Estimation of distribution
                 programming, Adaptive grammar, Bayesian network",
  isbn13 =       "978-3-319-45823-6",
  DOI =          "doi:10.1007/978-3-319-45823-6_25",
  abstract =     "Structure of a grammar can influence how well a
                 Grammar-Based Genetic Programming system solves a given
                 problem but it is not obvious to design the structure
                 of a grammar, especially when the problem is large. In
                 this paper, our proposed Bayesian Grammar-Based Genetic
                 Programming with Hierarchical Learning (BGBGP-HL)
                 examines the grammar and builds new rules on the
                 existing grammar structure during evolution. Once our
                 system successfully finds the good solution(s), the
                 adapted grammar will provide a grammar-based
                 probabilistic model to the generation process of
                 optimal solution(s). Moreover, our system can
                 automatically discover new hierarchical knowledge (i.e.
                 how the rules are structurally combined) which composes
                 of multiple production rules in the original grammar.
                 In the case study using deceptive royal tree problem,
                 our evaluation shows that BGBGP-HL achieves the best
                 performance among the competitors while it is capable
                 of composing hierarchical knowledge. Compared to other
                 algorithms, search performance of BGBGP-HL is shown to
                 be more robust against deceptiveness and complexity of
                 the problem.",
  notes =        "PPSN2016 http://ppsn2016.org",
}

Genetic Programming entries for Pak-Kan Wong Man Leung Wong Kwong-Sak Leung

Citations