Using a Distance Metric on Genetic Programs to Understand Genetic Operators
Created by W.Langdon from
gp-bibliography.bib Revision:1.5776
- @InProceedings{oreilly:1997:dnGPugo2,
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author = "Una-May O'Reilly",
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title = "Using a Distance Metric on Genetic Programs to
Understand Genetic Operators",
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booktitle = "IEEE International Conference on Systems, Man, and
Cybernetics, Computational Cybernetics and Simulation",
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year = "1997",
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pages = "4092--4097",
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volume = "5",
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address = "Orlando, Florida, USA",
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month = "12-15 " # oct,
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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",
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ISBN = "0-7803-4053-1",
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URL = "
http://ieeexplore.ieee.org/iel4/4942/13793/00637337.pdf",
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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.",
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notes = "{"}fair crossover{"} (no 90/10 bias), {"}Height fair
crossover{"} and normal subtree crossover",
- }
Genetic Programming entries for
Una-May O'Reilly
Citations