Procedural Texture Evolution Using Multiobjective Optimization
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{ross:2004:NGC,
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author = "Brian J. Ross and Han Zhu",
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title = "Procedural Texture Evolution Using Multiobjective
Optimization",
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journal = "New Generation Computing",
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year = "2004",
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volume = "22",
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number = "3",
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pages = "271--293",
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month = sep,
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keywords = "genetic algorithms, genetic programming, Procedural
Textures, Multi-objective Optimisation",
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ISSN = "1882-7055",
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URL = "http://www.cosc.brocku.ca/files/downloads/research/cs0218.pdf",
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DOI = "doi:10.1007/BF03040964",
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size = "23 pages",
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abstract = "investigates the application of evolutionary
multi-objective optimisation to two-dimensional
procedural texture synthesis. Genetic programming is
used to evolve procedural texture formulae. Earlier
work used multiple feature tests during fitness
evaluation to rate how closely a candidate texture
matches visual characteristics of a target texture
image. These feature test scores were combined into an
overall fitness score using a weighted sum. This paper
improves this research by replacing the weighted sum
with a Pareto ranking scheme, which preserves the
independence of feature tests during fitness
evaluation. Three experiments were performed: a pure
Pareto ranking scheme, and two Pareto experiments
enhanced with parameterless population divergence
strategies. One divergence strategy is similar to that
used by the NSGA-II system, and scores individuals
using their nearest-neighbour distance in
feature-space. The other strategy uses a normalised,
ranked abstraction of nearest neighbour distance. A
result of this work is that acceptable textures can be
evolved much more efficiently and with less user
intervention with MOP evolution than compared to the
weighted sum approach. Although the final acceptability
of a texture is ultimately a subjective decision of the
user, the proposed use of multi-objective evolution is
useful for generating for the user a diverse assortment
of possibilities that reflect the important features of
interest.",
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notes = "cs0218.pdf refers to Brock University technical report
#CS-02-18 July 2002.
LilGP 1.1
Ohmsha Ltd",
- }
Genetic Programming entries for
Brian J Ross
Han Zhu
Citations