Abstract:
Motivated by the ability of living cells to form specific shapes
and structures, we present a computational approach using
distributed genetic programming to discover cell-cell
interaction rules for automated shape composition. The key
concept is to evolve local rules that direct virtual cells to
produce a self-organizing behavior that leads to the formation
of a macroscopic, user-defined shape. The interactions of
the virtual cells, called Morphogenic Primitives (MPs), are
based on chemotaxis-driven aggregation behaviors exhibited
by actual living cells. 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 functions are
derived via a distributed genetic programming process. A
fittness 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 a distributed
genetic programming method to discover the chemical fields
needed to produce macroscopic shapes from simple
aggregating primitives.
