Fitness landscape analysis for evolutionary non-photorealistic rendering

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Riley:2010:cec,

• author = "Jeff Riley and Vic Ciesielski",
• title = "Fitness landscape analysis for evolutionary non-photorealistic rendering",
• booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)",
• year = "2010",
• address = "Barcelona, Spain",
• month = "18-23 " # jul,
• publisher = "IEEE Press",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-1-4244-6910-9",
• 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.",
• DOI = "doi:10.1109/CEC.2010.5586013",
• notes = "WCCI 2010. Also known as \cite{5586013}",

}

Genetic Programming entries for Jeff Riley Victor Ciesielski

Citations