Evolvable 3D Modeling for Model-Based Object Recognition Systems
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InCollection{kinnear:nguyen,
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title = "Evolvable {3D} Modeling for Model-Based Object
Recognition Systems",
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author = "Thang Nguyen and Thomas Huang",
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booktitle = "Advances in Genetic Programming",
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publisher = "MIT Press",
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editor = "Kenneth E. {Kinnear, Jr.}",
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year = "1994",
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chapter = "22",
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pages = "459--475",
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keywords = "genetic algorithms, genetic programming",
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URL = "http://www.amazon.co.uk/Advances-Genetic-Programming-Complex-Adaptive/dp/0262111888",
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URL = "http://cognet.mit.edu/sites/default/files/books/9780262277181/pdfs/9780262277181_chap22.pdf",
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DOI = "doi:10.7551/mitpress/1108.003.0028",
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size = "17 pages",
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abstract = "This paper presents a system that evolves 3D models
over time, eventually producing novel models that are
more desirable than initial models. The algorithm
starts with some crude models given by the user, or
randomly-generated models from a given model-grammar
with generic design rules and loose constraints. The
underlying philosophy here is of gradually evolving the
initial models into better models over many
generations. There is a close analog in the evolution
of species where better-fit species gradually emerge
and form specialised niches, a highly efficient process
of complex structural and functional optimization. Our
simulation results for 3D jet aircraft model design
illustrate that this approach to model design and
refinement is feasible and effective. The intended
application domain is for automatic object recognition
system, though the model fitness criteria is currently
determined by user interactive selection.",
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notes = "GP on very small populations (10). Very powefull,
aircraft design, primatives
see also
http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/ftp.io.com/papers/gp-3D-modeling.ps.Z
\cite{nguyen:emsat3d}
Part of \cite{kinnear:book}",
- }
Genetic Programming entries for
Thang C Nguyen
Thomas S Huang
Citations