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.
