Automated shape composition based on cell biology and distributed genetic programming

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 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 a distributed genetic programming method to discover the chemical fields needed to produce macroscopic shapes from simple aggregating primitives.

Self-organizing primitives for automated shape composition

“…The cell simulations that led to the definition of the field functions consisted of 500 primitives, where each primitive has a radius of 4.5 or 5 (depending on which shape we were trying to make) units 1 . The MPs move in a toroidal computational environment of 500×500 units.…”

“…Each 1000-time-step MP simulation requires approximately 4-CPU minutes on 1.8 GHz Opteron processor. We perform distributed GP calculations on an 8-node Linux cluster [1]. Therefore each 100-individual generation requires approximately one hour of actual time to compute on our cluster.…”

Evolution of a digital organism playing Go

“…Roggen and Federici (2004) evolved patterns of different size ranging from 8×8 to 128×128 cells [14]. A variety of similar experiments was presented in [15] and [16]. A real-world application of DOs is the creation of stable architectural structures.…”

An Emergent System for Self-Aligning and Self-Organizing Shape Primitives

“…We have implemented an N-slave, 1-master, steadystate genetic-programming model, with the master process adaptively distributing individuals among the slave processes [1], based on Unix shell scripting [5]. Open Beagle [17], a C++ evolutionary computing framework, has been utilized and altered to function as the distributed genetic programming framework.…”