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

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@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

Citations