Complexity Compression and Evolution
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Nordin:1995:cce,
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author = "Peter Nordin and Wolfgang Banzhaf",
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title = "Complexity Compression and Evolution",
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booktitle = "Genetic Algorithms: Proceedings of the Sixth
International Conference (ICGA95)",
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year = "1995",
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editor = "Larry J. Eshelman",
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pages = "310--317",
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address = "Pittsburgh, PA, USA",
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publisher_address = "San Francisco, CA, USA",
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month = "15-19 " # jul,
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publisher = "Morgan Kaufmann",
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keywords = "genetic algorithms, genetic programming",
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ISBN = "1-55860-370-0",
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URL = "ftp://lumpi.informatik.uni-dortmund.de/pub/biocomp/papers/icga95-1.ps.gz",
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URL = "http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.57.2133",
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size = "8 pages",
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abstract = "Compression of information is an important concept in
the theory of learning. We argue for the hypothesis
that there is an inherent compression pressure towards
short, elegant and general solutions in a genetic
programming system and other variable length
evolutionary algorithms. This pressure becomes visible
if the size or complexity of solutions are measured
without non-effective code segments called introns. The
built in parsimony pressure effects complex fitness
functions crossover probability, generality, maximum
depth or length of solutions, explicit parsimony,
granularity of fitness function, initialization depth
or length, and modularization. Some of these effects
are positive and some are negative. In this work we
provide a basis for an analysis of these effects and
suggestions to overcome the negative implications in
order to obtain the balance needed for successful
evolution. An empirical investigation that supports our
hypothesis is also presented.",
- }
Genetic Programming entries for
Peter Nordin
Wolfgang Banzhaf
Citations