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GECCO
2010
Springer

Evolving CPPNs to grow three-dimensional physical structures

9 years 4 months ago
Evolving CPPNs to grow three-dimensional physical structures
The majority of work in the field of evolutionary robotics concerns itself with evolving control strategies for human designed or bio-mimicked robot morphologies. However, there are reasons why co-evolving morphology along with control may provide a better path towards realizing intelligent agents. Towards this goal, a novel method for evolving three-dimensional physical structures using CPPN-NEAT is introduced which is capable of producing artifacts that capture the non-obvious yet close relationship between function and physical structure. Moreover, it is shown how more fit solutions can be achieved with less computational effort by using growth and environmental CPPN input parameters as well as incremental changes in resolution. Categories and Subject Descriptors I.2.9 [Computing Methodologies]: Artificial Intelligence— Robotics General Terms Experimentation Keywords Evolutionary robotics, Generative and Developmental Systems
Joshua E. Auerbach, Josh C. Bongard
Added 19 Jul 2010
Updated 19 Jul 2010
Type Conference
Year 2010
Where GECCO
Authors Joshua E. Auerbach, Josh C. Bongard
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