Fitness landscape analysis for evolutionary non-photorealistic rendering
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- @InProceedings{Riley:2010:cec,
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author = "Jeff Riley and Vic Ciesielski",
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title = "Fitness landscape analysis for evolutionary
non-photorealistic rendering",
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booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)",
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year = "2010",
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address = "Barcelona, Spain",
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month = "18-23 " # jul,
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publisher = "IEEE Press",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-1-4244-6910-9",
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abstract = "The best evolutionary approach can be a difficult
problem. In this work we have investigated two
evolutionary representations to evolve
non-photorealistic renderings: a variable-length
classic genetic algorithm representation, and a
tree-based genetic algorithm representation. These
representations exhibit very different convergence
behaviour, and despite considerable exploration of
parameters the classic genetic algorithm was not
competitive with the tree-based approach for the
problem studied in this work. The aim of the work
presented in this paper was to investigate whether
analysis of the fitness landscapes described by the
different representations can explain the difference in
performance. We used several current fitness landscape
measures to analyse the fitness landscapes, and found
that one of the measures suggests there is a
correlation between search performance and the fitness
landscape.",
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DOI = "doi:10.1109/CEC.2010.5586013",
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notes = "WCCI 2010. Also known as \cite{5586013}",
- }
Genetic Programming entries for
Jeff Riley
Victor Ciesielski
Citations