Artificial evolution for computer graphics
Proceedings of the 18th annual conference on Computer graphics and interactive techniques
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
UNIX distributed programming
SIGGRAPH '94 Proceedings of the 21st annual conference on Computer graphics and interactive techniques
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Artificial Life
Creative evolutionary systems
Experimental Investigation of Three Distributed Genetic Programming Models
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
A Genetic Algorithm for Channel Routing Problem
Proceedings of the 5th International Conference on Genetic Algorithms
A multiple-mechanism developmental model for defining self-organizing geometric structures
A multiple-mechanism developmental model for defining self-organizing geometric structures
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Unwitting distributed genetic programming via asynchronous JavaScript and XML
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A computational system for investigating chemotaxis-based cell aggregation
ECAL'07 Proceedings of the 9th European conference on Advances in artificial life
Learning to rank using evolutionary computation: immune programming or genetic programming?
Proceedings of the 18th ACM conference on Information and knowledge management
Morphologies of self-organizing swarms in 3D swarm chemistry
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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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-de.ned 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.