The Importance of Local Search: A Grammar Based Approach to Environmental Time Series Modelling

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@InCollection{hoang:2005:GPTP,
  author =       "Tuan Hao Hoang and Nguyen Xuan Hoai and 
                 R. I. (Bob) McKay and Daryl Essam",
  title =        "The Importance of Local Search: A Grammar Based
                 Approach to Environmental Time Series Modelling",
  booktitle =    "Genetic Programming Theory and Practice {III}",
  year =         "2005",
  editor =       "Tina Yu and Rick L. Riolo and Bill Worzel",
  volume =       "9",
  series =       "Genetic Programming",
  chapter =      "11",
  pages =        "159--175",
  address =      "Ann Arbor",
  month =        "12-14 " # may,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, local search,
                 insertion, deletion, grammar guided, tree adjoining
                 grammar, ecological modelling, time series",
  ISBN =         "0-387-28110-X",
  DOI =          "doi:10.1007/0-387-28111-8_11",
  size =         "17 pages",
  abstract =     "Standard Genetic Programming operators are highly
                 disruptive, with the concomitant risk that it may be
                 difficult to converge to an optimal structure. The Tree
                 Adjoining Grammar (TAG) formalism provides a more
                 flexible Genetic Programming tree representation which
                 supports a wide range of operators while retaining the
                 advantages of tree-based representation. In particular,
                 minimal-change point insertion and deletion operators
                 may be defined. Previous work has shown that point
                 insertion and deletion, used as local search operators,
                 can dramatically reduce search effort in a range of
                 standard problems. Here, we evaluate the effect of
                 local search with these operators on a real-World
                 ecological time series modelling problem. For the same
                 search effort, TAG-based GP with the local search
                 operators generates solutions with significantly lower
                 training set error. The results are equivocal on test
                 set error, local search generating larger individuals
                 which generalise only a little better than the less
                 accurate solutions given by the original algorithm.",
  notes =        "part of \cite{yu:2005:GPTP} Published Jan 2006 after
                 the workshop",
}

Genetic Programming entries for Tuan-Hao Hoang Nguyen Xuan Hoai R I (Bob) McKay Daryl Essam

Citations