Automatic Structure Generation using Genetic Programming and Fractal Geometry

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@MastersThesis{Bergen:mastersthesis,
  author =       "Steve Bergen",
  title =        "Automatic Structure Generation using Genetic
                 Programming and Fractal Geometry",
  school =       "Brock University",
  year =         "2011",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "https://dr.library.brocku.ca/bitstream/handle/10464/3916/Brock_Bergen_Raphael_2011.pdf",
  URL =          "http://hdl.handle.net/10464/3916",
  size =         "127 pages",
  abstract =     "Three dimensional model design is a well-known and
                 studied field, with numerous real-world applications.
                 However, the manual construction of these models can
                 often be time-consuming to the average user, despite
                 the advantages offered through computational advances.
                 This thesis presents an approach to the design of 3D
                 structures using evolutionary computation and
                 L-systems, which involves the automated production of
                 such designs using a strict set of fitness functions.
                 These functions focus on the geometric properties of
                 the models produced, as well as their quantifiable
                 aesthetic value - a topic which has not been widely
                 investigated with respect to 3D models. New extensions
                 to existing aesthetic measures are discussed and
                 implemented in the presented system in order to produce
                 designs which are visually pleasing. The system itself
                 facilitates the construction of models requiring
                 minimal user initialization and no user-based feedback
                 throughout the evolutionary cycle. The genetic
                 programming evolved models are shown to satisfy
                 multiple criteria, conveying a relationship between
                 their assigned aesthetic value and their perceived
                 aesthetic value. Exploration into the applicability and
                 effectiveness of a multi-objective approach to the
                 problem is also presented, with a focus on both
                 performance and visual results. Although subjective,
                 these results o er insight into future applications and
                 study in the field of computational aesthetics and
                 automated structure design.",
}

Genetic Programming entries for Steven Bergen

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