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Nature Machine Intelligence

  • Kudithipudi, D., Aguilar-Simon, M., Babb, J., Bazhenov, M., Blackiston, D., Bongard, J., Brna, A.P., Chakravarthi Raja, S., Cheney, N., Clune, J. and Daram, A., 2022. Biological underpinnings for lifelong learning machines. Nature Machine Intelligence, 4(3), pp.196-210. [ www ]
  • Miikkulainen, R. and Forrest, S., 2021. A biological perspective on evolutionary computation. Nature Machine Intelligence, 3(1), pp.9-15. [ www | pdf ] ( EC )

Shah, D.S., Powers, J.P., Tilton, L.G., Kriegman, S., Bongard, J. and Kramer-Bottiglio, R., 2021. A soft robot that adapts to environments through shape change. Nature Machine Intelligence, 3(1), pp.51-59. [ www | Julia ] (HC | NES | ER)

Weiel, M., Götz, M., Klein, A., Coquelin, D., Floca, R. and Schug, A., 2021. Dynamic particle swarm optimization of biomolecular simulation parameters with flexible objective functions. Nature Machine Intelligence, pp.1-8. [ www ]

2020

Tanneberg, D., Rueckert, E. and Peters, J., 2020. Evolutionary training and abstraction yields algorithmic generalization of neural computers. Nature Machine Intelligence, 2(12), pp.753-763. [ www ]

Miriyev, A. and Kovač, M., 2020. Skills for physical artificial intelligence. Nature Machine Intelligence, 2(11), pp.658-660. [ www ] (ER)

Birattari, M., Ligot, A. and Hasselmann, K., 2020. Disentangling automatic and semi-automatic approaches to the optimization-based design of control software for robot swarms. Nature Machine Intelligence, 2(9), pp.494-499. [ www ]

2019

Stanley, K.O., Clune, J., Lehman, J. and Miikkulainen, R., 2019. Designing neural networks through neuroevolution. Nature Machine Intelligence, 1(1), pp.24-35. [ www ]

Kriegman, S., 2019. Why virtual creatures matter. Nature Machine Intelligence, 1(10), pp.492-492. [ www ]

Howard, D., Eiben, A.E., Kennedy, D.F., Mouret, J.B., Valencia, P. and Winkler, D., 2019. Evolving embodied intelligence from materials to machines. Nature Machine Intelligence, 1(1), pp.12-19. [ www ]