Multiobjective Parsimony Enforcement for Superior Generalisation Performance
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{berstein:2004:mpefsgp,
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title = "Multiobjective Parsimony Enforcement for Superior
Generalisation Performance",
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author = "Yaniv Bernstein and Xiaodong Li and Vic Ciesielski and
Andy Song",
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pages = "83--89",
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booktitle = "Proceedings of the 2004 IEEE Congress on Evolutionary
Computation",
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year = "2004",
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publisher = "IEEE Press",
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month = "20-23 " # jun,
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address = "Portland, Oregon",
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ISBN = "0-7803-8515-2",
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keywords = "genetic algorithms, genetic programming,
Multiobjective evolutionary algorithms, Combinatorial
\& numerical optimization",
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URL = "http://goanna.cs.rmit.edu.au/~ybernste/papers/Bernstein_CEC_2004.pdf",
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DOI = "doi:10.1109/CEC.2004.1330841",
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size = "7 pages",
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abstract = "Program Bloat - the phenomenon of ever-increasing
program size during a GP run - is a recognised and
widespread problem. Traditional techniques to combat
program bloat are program size limitations or parsimony
pressure (penalty functions). These techniques suffer
from a number of problems, in particular their reliance
on parameters whose optimal values it is difficult to a
priori determine. In this paper we introduce POPE-GP, a
system that makes use of the NSGA-II multiobjective
evolutionary algorithm as an alternative,
parameter-free technique for eliminating program bloat.
We test it on a classification problem and find that
while vastly reducing program size, it does improve
generalisation performance.",
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notes = "CEC 2004 - A joint meeting of the IEEE, the EPS, and
the IEE.",
- }
Genetic Programming entries for
Yaniv Bernstein
Xiaodong Li
Victor Ciesielski
Andy Song
Citations