Hierarchical Self-Organization in Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Rosca:1994:hsoGP,
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author = "Justinian P. Rosca and Dana H. Ballard",
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title = "Hierarchical Self-Organization in Genetic
Programming",
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booktitle = "Proceedings of the Eleventh International Conference
on Machine Learning",
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year = "1994",
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pages = "251--258",
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address = "New Brunswick, NJ, USA",
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month = jul # " 10-13",
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publisher = "Morgan Kaufmann",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-1-55860-335-6",
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URL = "ftp://ftp.cs.rochester.edu/pub/u/rosca/gp/94.ml.hierarchical_so_gp.ps.gz",
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URL = "https://www.sciencedirect.com/science/article/pii/B9781558603356500386",
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DOI = "doi:10.1016/B978-1-55860-335-6.50038-6",
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size = "8 pages",
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abstract = "This paper presents an approach to automatic discovery
of functions in Genetic Programming. The approach is
based on discovery of useful building blocks by
analyzing the evolution trace, generalizing blocks to
define new functions, and finally adapting the problem
representation on-the-fly. Adapting the representation
determines a hierarchical organization of the extended
function set which enables a restructuring of the
search space so that solutions can be found more
easily. Measures of complexity of solution trees are
defined for an adaptive representation framework. The
minimum description length principle is applied to
justify the feasibility of approaches based on a
hierarchy of discovered functions and to suggest
alternative ways of defining a problem's fitness
function. Preliminary empirical results are
presented.",
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notes = "Also known as \cite{ROSCA1994251}
",
- }
Genetic Programming entries for
Justinian Rosca
Dana H Ballard
Citations