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Nature Materials

  • Fernandes, M.C., Aizenberg, J., Weaver, J.C. and Bertoldi, K., 2021. Mechanically robust lattices inspired by deep-sea glass sponges. Nature Materials, 20(2), pp.237-241. [ www ] ( CMA-ES | Continuous Optimization )
    • "We maximized the objective function using finite element simulations coupled to a Python implementation of the Covariance Matrix Adaptation Evolution Strategy algorithm (CMA-ES)."
    • "We employ an optimization algorithm to identify the beam configuration in a diagonally reinforced square lattice that achieves the highest critical load, revealing—unexpectedly—that the skeletal system of E. aspergillum is very close to this design optimum. Since it is a derivative free algorithm, CMA-ES is well suited for optimization problems of high dimensionality and non-linear parameter topology."
      • Hansen, N., Akimoto, Y. & Baudis, P. CMA-ES/pycma: r3.0.3. https://doi.org/10.5281/zenodo.2559634 (2019).
      • Hansen, N., Müller, S. D., and Koumoutsakos, P. (2003). Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (cma-es). Evolutionary Computation, 11(1):1–18.
  • Miskin, M.Z. and Jaeger, H.M., 2013. Adapting granular materials through artificial evolution. Nature Materials, 12(4), pp.326-331. [ www ] ( CMA-ES | Continuous Optimization )
    • "We used an evolutionary algorithm based on the covariance matrix adaptation evolution strategy (CMA-ES)."
      • Eiben, A. E. & Smith, J. E. Introduction to Evolutionary Computing (Springer, 2003).
      • Hansen, N., Muller, S. D. & Koumoutsakos, P. Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). Evol. Comput. 11, 1–18 (2003).
      • Lipson, H. & Pollack, J. B. Automatic design and manufacture of robotic lifeforms. Nature 406, 974–978 (2000).
      • Oganov, A. R., Lyakhov, A. O. & Valle, M. How evolutionary crystal structure prediction works—and why. Accounts Chem. Res. 44, 227–237 (2011).
    • Highly evolved grains
  • Rogers, E.T., Lindberg, J., Roy, T., Savo, S., Chad, J.E., Dennis, M.R. and Zheludev, N.I., 2012. A super-oscillatory lens optical microscope for subwavelength imaging. Nature Materials, 11(5), pp.432-435. [ www ] ( PSO | Continuous Optimization )
    • "To design the binary mask the radial coordinate was divided into N concentric annuli, each of which had either unit or zero transmittance. The mask was optimized using the binary particle swarm optimization."
      • Jin, N. & Rahmat-Samii, Y. Advances in particle swarm optimization for antenna designs: Real-number, binary, single-objective and multiobjective implementations. IEEE Trans. Antenn. Propag. 55, 556–567 (2007).
  • Hart, G.L., Blum, V., Walorski, M.J. and Zunger, A., 2005. Evolutionary approach for determining first-principles hamiltonians. Nature Materials, 4(5), pp.391-394. [ www ] ( GA | Discrete Optimization )
    • "Here we show how genetic algorithms, mimicking biological evolution ('survival of the fittest'), can be used to distil reliable model hamiltonian parameters from a database of first-principles calculations."
      • Michalewicz, Z. & Fogel, D. B. How to Solve it: Modern Heuristics (Springer, Berlin, 2000).