Using a Distance Metric on Genetic Programs to Understand Genetic Operators

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{oreilly:1997:dnGPugo2,

• author = "Una-May O'Reilly",
• title = "Using a Distance Metric on Genetic Programs to Understand Genetic Operators",
• booktitle = "IEEE International Conference on Systems, Man, and Cybernetics, Computational Cybernetics and Simulation",
• year = "1997",
• pages = "4092--4097",
• volume = "5",
• address = "Orlando, Florida, USA",
• month = "12-15 " # oct,
• keywords = "genetic algorithms, genetic programming, distance metric, genetic programs, genetic operators, edit distance, syntactic difference, multiplexor, crossover operators, population, best individuals, run performance, search, trees",
• ISBN = "0-7803-4053-1",
• URL = "http://ieeexplore.ieee.org/iel4/4942/13793/00637337.pdf",
• DOI = "doi:10.1109/ICSMC.1997.637337",
• size = "6 pages",
• 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 further investigation into the use of edit distance.",
• notes = "'fair crossover' (no 90/10 bias), 'Height fair crossover' and normal subtree crossover",

}

Genetic Programming entries for Una-May O'Reilly

Citations