Marching cubes: A high resolution 3D surface construction algorithm
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Evolving neural networks through augmenting topologies
Evolutionary Computation
A Taxonomy for artificial embryogeny
Artificial Life
Compositional pattern producing networks: A novel abstraction of development
Genetic Programming and Evolvable Machines
The sensitivity of HyperNEAT to different geometric representations of a problem
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Evolving 3d morphology and behavior by competition
Artificial Life
Morphological evolution of freeform robots
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Evolving CPPNs to grow three-dimensional physical structures
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Picbreeder: A case study in collaborative evolutionary exploration of design space
Evolutionary Computation
On the Performance of Indirect Encoding Across the Continuum of Regularity
IEEE Transactions on Evolutionary Computation
3D game model and texture generation using interactive genetic algorithm
Proceedings of the Workshop at SIGGRAPH Asia
Learning aesthetic judgements in evolutionary art systems
Genetic Programming and Evolvable Machines
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This paper introduces an algorithm for evolving 3D objects with a generative encoding that abstracts how biological morphologies are produced. Evolving interesting 3D objects is useful in many disciplines, including artistic design (e.g. sculpture), engineering (e.g. robotics, architecture, or product design), and biology (e.g. for investigating morphological evolution). A critical element in evolving 3D objects is the representation, which strongly influences the types of objects produced. In 2007 a representation was introduced called Compositional Pattern Producing Networks (CPPN), which abstracts how natural phenotypes are generated. To date, however, the ability of CPPNs to create 3D objects has barely been explored. Here we present a new way to create 3D objects with CPPNs. Experiments with both interactive and target-based evolution demonstrate that CPPNs show potential in generating interesting, complex, 3D objects. We further show that changing the information provided to CPPNs and the functions allowed in their genomes biases the types of objects produced. Finally, we validate that the objects transfer well from simulation to the real-world by printing them with a 3D printer. Overall, this paper shows that evolving objects with encodings based on concepts from biological development can be a powerful way to evolve complex, interesting objects, which should be of use in fields as diverse as art, engineering, and biology.