Self-organizing primitives for automated shape composition

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.