Abstract
A method for synthesizing grammatical models of natural plants is presented. It is an attempt at solving the inverse problem of generating the model that best describes a plant growth process, presented in a set of 2D pictures. A geometric study is undertaken before translating it into grammatical meaning; a genetic algorithm, coupled with a deterministic rule generation algorithm, is then applied for navigating through the space of possible solutions. Preliminary results together with a detailed description of the method are presented.
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Da Costa, L.E., Landry, JA. (2005). Generating grammatical plant models with genetic algorithms. In: Ribeiro, B., Albrecht, R.F., Dobnikar, A., Pearson, D.W., Steele, N.C. (eds) Adaptive and Natural Computing Algorithms. Springer, Vienna. https://doi.org/10.1007/3-211-27389-1_55
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DOI: https://doi.org/10.1007/3-211-27389-1_55
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-24934-5
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