Self-organizing primitives for automated shape composition

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  author =       "Linge Bai and Manolya Eyiyurekli and David E. Breen",
  title =        "Self-organizing primitives for automated shape
  booktitle =    "IEEE International Conference on Shape Modeling and
                 Applications, SMI 2008",
  year =         "2008",
  month =        jun,
  pages =        "147--154",
  keywords =     "genetic algorithms, genetic programming, automated
                 shape composition, cell behavior, chemical-field-driven
                 aggregation, chemotaxis-driven aggregation behavior,
                 cumulative chemical field, evolutionary computing
                 process, fitness measure, macroscopic shape,
                 mathematical function, morphogenic primitives,
                 self-organizing primitive, shape formation, shape
                 modeling, structure formation, computational geometry",
  DOI =          "doi:10.1109/SMI.2008.4547962",
  abstract =     "Motivated by the ability of living cells to form into
                 specific shapes and structures, we present a new
                 approach to shape modeling based on self-organizing
                 primitives whose behaviors are derived via genetic
                 programming. The key concept of our approach is that
                 local interactions between the primitives direct them
                 to come together into a macroscopic shape. The
                 interactions of the primitives, called morphogenic
                 primitives (MP), are based on the chemotaxis-driven
                 aggregation behaviors exhibited by actual living cells.
                 Here, cells emit a chemical into their environment.
                 Each cell responds to the stimulus by moving in the
                 direction of the gradient of the cumulative chemical
                 field detected at its surface. MPs, though, do not
                 attempt to completely mimic the behavior of real cells.
                 The chemical fields are explicitly defined as
                 mathematical functions and are not necessarily
                 physically accurate. The explicit mathematical form of
                 the chemical field functions are derived via genetic
                 programming (GP), an evolutionary computing process
                 that evolves a population of functions. A fitness
                 measure, based on the shape that emerges from the
                 chemical-field-driven aggregation, determines which
                 functions will be passed along to later generations.
                 This paper describes the cell interactions of MPs and
                 the GP-based method used to define the chemical field
                 functions needed to produce user- specified shapes from
                 simple aggregating primitives.",
  notes =        "Also known as \cite{4547962}",

Genetic Programming entries for Linge Bai Manolya Eyiyurekli David E Breen