Preventing Early Convergence in Genetic Programming by Replacing Similar Programs
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- @Misc{oai:CiteSeerPSU:451316,
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title = "Preventing Early Convergence in Genetic Programming by
Replacing Similar Programs",
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author = "Dylan Mawhinney",
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year = "2000",
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month = oct # "~31",
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school = "RMIT",
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address = "Australia",
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keywords = "genetic algorithms, genetic programming",
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URL = "
http://www.cs.rmit.edu.au/~vc/papers/mahwinney-hons.ps.gz",
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URL = "
http://citeseer.ist.psu.edu/451316.html",
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oai:CiteSeerPSU:32228; oai:CiteSeerPSU:189578;
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oai:CiteSeerPSU:269924; oai:CiteSeerPSU:125377;
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annote = "The Pennsylvania State University CiteSeer Archives",
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language = "en",
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oai = "oai:CiteSeerPSU:451316",
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rights = "unrestricted",
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size = "39 pages",
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abstract = "Genetic programming is a means of automatically
evolving programs to perform a particular task or solve
a particular problem using the Darwinian principle of
survival of the fittest. Many genetic programming
problems can suffer from early convergence, that is,
the genetic programming run terminates before the
optimal program has evolved. Early convergence is a
hindrance to genetic programming especially for
problems which need signi cant amounts of computing
time. This project describes a method of preventing
early convergence by replacing similar programs. A
percentage of the most similar programs are replaced by
randomly generated programs. This method uses the
number of changes reported by the UNIX program diff to
estimate how similar a program is to the rest of the
population. We performed experiments using no
replacement and replacement on the MAX problem, a
problem known to suffer from early convergence, and the
Robocup simulator league domain. Using a replacement
rate of 10% in the MAX domain, increased the success
rate from 16% (using no replacement) to 42%. Performing
similarity replacement in the Robocup domain increased
the number of runs which obtained successful players,
from 2 out of the 5 runs using no replacement, to 4 out
of the 5 runs using 10% replacement. The quality of the
players in the successful runs was also improved.
Performing replacement every 2nd, 5th, or 10th
generation did not significantly reduce the number of
successful runs in the MAX domain when using a
replacement rate of 10%. Replacing a percentage of the
most similar programs prevented early convergence more
often than when no replacement was used. Our results
suggest that performing similarity replacement is
worthwhile in problems where the cost of computing the
t...",
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notes = "Honours thesis? RMIT.EDU.AU down at present See also
\cite{ciesielski:2002:poecigpbrosp}",
- }
Genetic Programming entries for
Dylan Mawhinney
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