English

Evolving Programmable Computational Metamaterials

Emerging Technologies 2022-06-07 v2 Neural and Evolutionary Computing

Abstract

Granular metamaterials are a promising choice for the realization of mechanical computing devices. As preliminary evidence of this, we demonstrate here how to embed Boolean logic gates (AND and XOR) into a granular metamaterial by evolving where particular grains are placed in the material. Our results confirm the existence of gradients of increasing "AND-ness" and "XOR-ness" within the space of possible materials that can be followed by evolutionary search. We measure the computational functionality of a material by probing how it transforms bits encoded as vibrations with zero or non-zero amplitude. We compared the evolution of materials built from mass-contrasting particles and materials built from stiffness-contrasting particles, and found that the latter were more evolvable. We believe this work may pave the way toward evolutionary design of increasingly sophisticated, programmable, and computationally dense metamaterials with certain advantages over more traditional computational substrates.

Keywords

Cite

@article{arxiv.2204.08651,
  title  = {Evolving Programmable Computational Metamaterials},
  author = {Atoosa Parsa and Dong Wang and Corey S. O'Hern and Mark D. Shattuck and Rebecca Kramer-Bottiglio and Josh Bongard},
  journal= {arXiv preprint arXiv:2204.08651},
  year   = {2022}
}

Comments

Accepted to the Genetic and Evolutionary Computation Conference 2022 (GECCO '22)

R2 v1 2026-06-24T10:51:40.652Z