Trivial Geography in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8120

@InCollection{spector:2005:GPTP,

• author = "Lee Spector and Jon Klein",
• title = "Trivial Geography in Genetic Programming",
• 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 = "8",
• pages = "109--123",
• address = "Ann Arbor",
• month = "12-14 " # may,
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, geography, locality, demes, symbolic regression, quantum computing",
• ISBN = "0-387-28110-X",
• URL = "http://hampshire.edu/lspector/pubs/trivial-geography-toappear.pdf",
• DOI = "doi:10.1007/0-387-28111-8_8",
• size = "15 pages",
• abstract = "Geographical distribution is widely held to be a major determinant of evolutionary dynamics. Correspondingly, genetic programming theorists and practitioners have long developed, used, and studied systems in which populations are structured in quasi-geographical ways. Here we show that a remarkably simple version of this idea produces surprisingly dramatic improvements in problem-solving performance on a suite of test problems. The scheme is trivial to implement, in some cases involving little more than the addition of a modulus operation in the population access function, and yet it provides significant benefits on all of our test problems (ten symbolic regression problems and a quantum computing problem). We recommend the broader adoption of this form of 'trivial geography' in genetic programming systems.",
• notes = "part of \cite{yu:2005:GPTP} Published Jan 2006 after the workshop",

}

Genetic Programming entries for Lee Spector Jon Klein

Citations