A Fine-Grained View of GP Locality with Binary Decision Diagrams as Ant Phenotypes

Created by W.Langdon from gp-bibliography.bib Revision:1.2031

@InProceedings{mcdermott_etal:ppsn2010,
  author =       "James McDermott and Edgar Galvan-Lopez and 
                 Michael O'Neill",
  title =        "A Fine-Grained View of {GP} Locality with Binary
                 Decision Diagrams as Ant Phenotypes",
  booktitle =    "PPSN 2010 11th International Conference on Parallel
                 Problem Solving From Nature",
  pages =        "164--173",
  year =         "2010",
  volume =       "6238",
  editor =       "Robert Schaefer and Carlos Cotta and 
                 Joanna Kolodziej and Guenter Rudolph",
  publisher =    "Springer",
  series =       "Lecture Notes in Computer Science",
  isbn13 =       "978-3-642-15843-8",
  address =      "Krakow, Poland",
  month =        "11-15 " # sep,
  keywords =     "genetic algorithms, genetic programming",
  doi =          "doi:10.1007/978-3-642-15844-5_17",
  abstract =     "The property that neighbouring genotypes tend to map
                 to neighbouring phenotypes, i.e. locality, is an
                 important criterion in the study of problem difficulty.
                 Locality is problematic in tree-based genetic
                 programming (GP), since typically there is no explicit
                 phenotype. Here, we define multiple phenotypes for the
                 artificial ant problem, and use them to describe a
                 novel fine-grained view of GP locality. This allows us
                 to identify the mapping from an ant's behavioural
                 phenotype to its concrete path as being inherently
                 non-local, and show that therefore alternative genetic
                 encodings and operators cannot make the problem easy.
                 We relate this to the results of evolutionary runs.",
  notes =        "Santa Fe trail Ant",
}

Genetic Programming entries for James McDermott Edgar Galvan Lopez Michael O'Neill