Using Scaffolding with Partial Call-Trees to Improve Search

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

@InProceedings{Alexander:2016:PPSN,
  author =       "Brad Alexander and Connie Pyromallis and 
                 George Lorenzetti and Brad Zacher",
  title =        "Using Scaffolding with Partial Call-Trees to Improve
                 Search",
  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 =        "324--334",
  address =      "Edinburgh",
  month =        "17-21 " # sep,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, Grammatical
                 evolution, Recursion",
  isbn13 =       "978-3-319-45823-6",
  DOI =          "doi:10.1007/978-3-319-45823-6_3",
  size =         "11 page",
  abstract =     "Recursive functions are an attractive target for
                 genetic programming because they can express complex
                 computation compactly. However, the need to
                 simultaneously discover correct recursive and base
                 cases in these functions is a major obstacle in the
                 evolutionary search process. To overcome these
                 obstacles two recent remedies have been proposed. The
                 first is Scaffolding which permits the recursive case
                 of a function to be evaluated independently of the base
                 case. The second is Call- Tree-Guided Genetic
                 Programming (CTGGP) which uses a partial call tree,
                 supplied by the user, to separately evolve the
                 parameter expressions for recursive calls. Used in
                 isolation, both of these approaches have been shown to
                 offer significant advantages in terms of search
                 performance. In this work we investigate the impact of
                 different combinations of these approaches. We find
                 that, on our benchmarks, CTGGP significantly
                 outperforms Scaffolding and that a combination CTGGP
                 and Scaffolding appears to produce further improvements
                 in worst-case performance.",
  notes =        "factorial, odd-evens, log2, Fibonacci and Fibonacci-3,
                 the nth Lucas number, the nth Pell number.

                 p331 'We ran our experiments on an AMD Opteron 6348
                 machine with 48 processors running at 2.8 GHz'

                 PPSN2016 http://ppsn2016.org",
}

Genetic Programming entries for Brad Alexander Connie Pyromallis George Lorenzetti Brad Zacher

Citations