Using a Distance Metric on Genetic Programs to
Understand Genetic Operators
U. O'Reilly. IEEE International Conference on Systems, Man, and
Cybernetics, Computational Cybernetics and Simulation, 5, стр. 4092--4097. Orlando, Florida, USA, (12-15 October 1997)
Аннотация
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.
%0 Conference Paper
%1 oreilly:1997:dnGPugo2
%A O'Reilly, Una-May
%B IEEE International Conference on Systems, Man, and
Cybernetics, Computational Cybernetics and Simulation
%C Orlando, Florida, USA
%D 1997
%K algorithms, best crossover difference, distance distance, edit genetic individuals, metric, multiplexor, operators, performance, population, programming, programs, run search, syntactic trees
%P 4092--4097
%T Using a Distance Metric on Genetic Programs to
Understand Genetic Operators
%U http://ieeexplore.ieee.org/iel4/4942/13793/00637337.pdf
%V 5
%X 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.
%@ 0-7803-4053-1
@inproceedings{oreilly:1997:dnGPugo2,
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.},
added-at = {2008-06-19T17:35:00.000+0200},
address = {Orlando, Florida, USA},
author = {O'Reilly, Una-May},
biburl = {https://www.bibsonomy.org/bibtex/2cde91a67de4006894ef231103f864a0e/brazovayeye},
booktitle = {IEEE International Conference on Systems, Man, and
Cybernetics, Computational Cybernetics and Simulation},
interhash = {122cb2be00ad79c6546c396f5f100417},
intrahash = {cde91a67de4006894ef231103f864a0e},
isbn = {0-7803-4053-1},
keywords = {algorithms, best crossover difference, distance distance, edit genetic individuals, metric, multiplexor, operators, performance, population, programming, programs, run search, syntactic trees},
month = {12-15 October},
notes = {{"}fair crossover{"} (no 90/10 bias), {"}Height fair
crossover{"} and normal subtree crossover},
pages = {4092--4097},
timestamp = {2008-06-19T17:49:05.000+0200},
title = {Using a Distance Metric on Genetic Programs to
Understand Genetic Operators},
url = {http://ieeexplore.ieee.org/iel4/4942/13793/00637337.pdf},
volume = 5,
year = 1997
}