Using a Distance Metric on Genetic Programs to Understand Genetic Operators

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@Unpublished{oreilly:1997:dmGPugo,

• author = "Una-May O'Reilly",
• title = "Using a Distance Metric on Genetic Programs to Understand Genetic Operators",
• editor = "Wolfgang Banzhaf and Inman Harvey and Hitoshi Iba and William Langdon and Una-May O'Reilly and Justinian Rosca and Byoung-Tak Zhang",
• note = "Position paper at the Workshop on Evolutionary Computation with Variable Size Representation at ICGA-97",
• month = "20 " # jul,
• year = "1997",
• address = "East Lansing, MI, USA",
• keywords = "genetic algorithms, genetic programming, variable size representation",
• abstract = "I describe a distance metric called ''edit'' distance which quantifies the syntactic difference between two genetic programs. In the context of one specific problem, the 6 bit multiplexor, I use the metric to analyze the amount of new material introduced by different crossover operators, the difference among the best individuals of a population and the difference among the best individuals and the rest of the population. The relationships between these data and run performance are imprecise but they are sufficiently interesting to encourage encourage further investigation into the use of edit distance.",
• notes = "http://web.archive.org/web/19971014081458/http://www.ai.mit.edu/people/unamay/icga-ws.html

  ",

• size = "4 pages. See \cite{oreilly:1997:dnGPugo2}",

}

Genetic Programming entries for Una-May O'Reilly

Citations