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Nature Communications

  • Gil-Fuster, E., Eisert, J. and Bravo-Prieto, C., 2024. Understanding quantum machine learning also requires rethinking generalization. Nature Communications, 15(1), pp.1-12. { code + CMA-ES }
    • "For all implementations, the training parameters were initialized randomly. The optimization method employed to update the parameters of the QCNN during training is the CMA-ES, a stochastic, derivative-free optimization strategy."
  • Le Fouest, S. and Mulleners, K., 2024. Optimal blade pitch control for enhanced vertical-axis wind turbine performance. Nature Communications, 15(1), p.2770. { NSGA-II }
    • Abstract: "We perform automated experiments using a scaled-down turbine model coupled to a genetic algorithm optimiser to identify optimal pitching kinematics at on- and off-design operating conditions."
  • Rudolph, M.S., Miller, J., Motlagh, D., Chen, J., Acharya, A. and Perdomo-Ortiz, A., 2023. Synergistic pretraining of parametrized quantum circuits via tensor networks. Nature Communications, 14(1), p.8367. { CMA-ES } *
  • Sosa-Carrillo, S., Galez, H., Napolitano, S., Bertaux, F. and Batt, G., 2022. Maximizing protein production by keeping cells at optimal secretory stress levels using real-time control approaches. bioRxiv, pp.2022-11. ( CMA-ES | Continuous Optimization )
    • "Parameter fitting was performed thanks to the CMA-ES algorithm using the pycma package from Hansen and colleagues."
  • Brea, J., Clayton, N.S. and Gerstner, W., 2023. Computational models of episodic-like memory in food-caching birds. Nature Communications, 14(1), p.2979. { CMA-ES }
    • "We optimize the approximate likelihood function with the CMA evolutionary strategy (CMA-ES). Because the variance of the approximate log-likelihood function the noise handling strategy of CMA-ES selects ... adaptively. This adaptivity saves computation time."
      • Hansen, N., Niederberger, A., Guzzella, L. & Koumoutsakos, P. A method for handling uncertainty in evolutionary optimization with an application to feedback control of combustion. IEEE Trans. Evol. Comput. 13, 180–197 (2009).
  • Ke, F., Yan, J., Niu, S., Wen, J., Yin, K., Yang, H., Wolf, N.R., Tzeng, Y.K., Karunadasa, H.I., Lee, Y.S. and Mao, W.L., 2022. Cesium-mediated electron redistribution and electron-electron interaction in high-pressure metallic CsPbI3. Nature Communications, 13(1), p.7067. [ www ] ( PSO | Continuous Optimization )
    • "Structure prediction of CsPbI3 at high pressure was performed via a global minimum search of the free energy surface by the swam intelligence-based CALYPSO method."
      • Wang, Y., Lv, J., Zhu, L. & Ma, Y. Crystal structure prediction via particle-swarm optimization. Phys. Rev. B 82, 094116 (2010).
      • Wang, Y. et al. Materials discovery via CALYPSO methodology. J. Phys. Condens. Matter 27, 203203 (2015).
  • Fang, H. and Jena, P., 2022. Argyrodite-type advanced lithium conductors and transport mechanisms beyond paddle-wheel effect. Nature Communications, 13(1), p.2078. [ www ] ( PSO | Continuous Optimization )
    • "The cluster-based structures are searched using Particle Swarm Optimization (PSO) algorithm (based on the PSO library of CALYPSO47) and density functional theory."
      • Wang, Y., Lv, J., Zhu, L. & Ma, Y. Crystal structure prediction via particle swarm optimization. Phys. Rev. B 82, 094116 (2010).
  • Liu, Y., Wang, R., Wang, Z., Li, D. and Cui, T., 2022. Formation of twelve-fold iodine coordination at high pressure. Nature Communications, 13(1), p.412. [ www ] ( PSO | Continuous Optimization )
    • "Here, based on the particle swarm optimization method and first-principles calculations, we report an exotically icosahedral cage-like hypercoordinated IN6 compound composed of N6 rings and an unusual iodine−nitrogen covalent bond network."
      • Wang, Y., Lv, J., Zhu, L. & Ma, Y. Crystal structure prediction via particle swarm optimization. Phys. Rev. B 82, 094116 (2010).
  • Rose, O., Johnson, J., Wang, B. and Ponce, C.R., 2021. Visual prototypes in the ventral stream are attuned to complexity and gaze behavior. Nature Communications, 12(1), p.6723. [ www ] ( CMA-ES | Continuous Optimization )
    • "Each image was then scored based on the inverse of its distance to the target vector; Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) was used to identify new candidate vectors for the next generation. The evolution continued until the mean norm of a given generation reached the same norm as that of the target vector."
      • Loshchilov, I. A computationally efficient limited memory CMA-ES for large scale optimization. in GECCO 2014 - Proceedings of the 2014 Genetic and Evolutionary Computation Conference 397–404 (Association for Computing Machinery, 2014). https://doi.org/10.1145/2576768.2598294
  • Kong, X., Kong, R., Orban, C., Wang, P., Zhang, S., Anderson, K., Holmes, A., Murray, J.D., Deco, G., van den Heuvel, M. and Yeo, B.T., 2021. Sensory-motor cortices shape functional connectivity dynamics in the human brain. Nature Communications, 12(1), p.6373. [ www ] ( CMA-ES | Continuous Optimization )
    • "Covariance matrix adaptation evolution strategy (CMA-ES) performed the best in the validation set."
      • Hansen, N. In Towards a New Evolutionary Computation (eds. Lozano, J. A., Larrañga, P., Inza, I. & Bengoetxea, E.) 75–102 (2006).
  • Gao, Y., Wu, M. and Jena, P., 2021. A family of ionic supersalts with covalent-like directionality and unconventional multiferroicity. Nature Communications, 12(1), p.1331. [ www ] ( PSO | Continuous Optimization )
    • "An unbiased swarm-intelligence structural method based on the particle swarm optimization (PSO) technique implemented in the Crystal structure AnaLYsis by Particle Swarm Optimization (CALYPSO) code is employed to search for low-energy structures of supersalts."
  • Hasselmann, K., Ligot, A., Ruddick, J. and Birattari, M., 2021. Empirical assessment and comparison of neuro-evolutionary methods for the automatic off-line design of robot swarms. Nature Communications, 12(1), pp.1-11. [ www ] (ER | SR)
  • Gussow, A.B., Park, A.E., Borges, A.L., Shmakov, S.A., Makarova, K.S., Wolf, Y.I., Bondy-Denomy, J. and Koonin, E.V., 2020. Machine-learning approach expands the repertoire of anti-CRISPR protein families. Nature Communications, 11(1), pp.1-12. [ www | Python ] (GA)
  • Zhang, X., Chan, F.K., Parthasarathy, T. and Gazzola, M., 2019. Modeling and simulation of complex dynamic musculoskeletal architectures. Nature Communications, 10(1), p.4825. [ www ] ( CMA-ES | Continuous Optimization )
    • "In order to identify the optimal design, we couple our solver with the Covariance Matrix Adaptation-Evolution Strategy algorithm (CMA-ES, Hansen et al.). The CMA-ES is a stochastic optimization algorithm that progressively samples generations of parameter vectors (population of bots characterized by different layouts) from a multivariate Gaussian distribution N. While there is no mathematical proof of convergence to global optimum, CMA-ES has proven reliable in dealing with multi-modal, low-dimensional continuous problems and has been employed in a range of engineering and biophysical applications."
      • 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).
      • Hansen, N. & Ros, R. Benchmarking a weighted negative covariance matrix update on the bbob-2010 noiseless testbed. In Proceedings of the 12th Annual Conference Companion on Genetic and Evolutionary Computation 1673–1680 (ACM, 2010).
      • Nguyen, D. M. & Hansen, N. Benchmarking cmaes-apop on the bbob noiseless testbed. In Proceedings of the Genetic and Evolutionary Computation Conference Companion 1756–1763 (ACM, 2017).
      • van Rees, W., Gazzola, M. & Koumoutsakos, P. Optimal shapes for anguilliform swimmers at intermediate reynolds numbers. J. Fluid Mech. 722, R3 (2013).
      • Gazzola, M., vanRees, W. & Koumoutsakos, P. C-start: optimal start of larval fish. J. Fluid Mech. 698, 5–18 (2012).
      • Gazzola, M., Tchieu, A., Alexeev, D., de Brauer, A. & Koumoutsakos, P. Learning to school in the presence of hydrodynamic interactions. J. Fluid Mech. 789, 726–749 (2015).
      • Chatelain, P., Gazzola, M., Kern, S. & Koumoutsakos, P. Optimization of aircraft wake alleviation schemes through an evolution strategy. Int. Conf. High Perform. Comput. Comput. Sci. 6649, 210–221 (2010). Springer.
  • Binion, J.D., Lier, E., Hand, T.H., Jiang, Z.H. and Werner, D.H., 2019. A metamaterial-enabled design enhancing decades-old short backfire antenna technology for space applications. Nature Communications, 10(1), p.108. [ www ] ( CMA-ES | Continuous Optimization )
    • "The Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) is a global optimization algorithm that has been shown to be well suited to finding solutions for a wide variety of electromagnetics problems. In this case, CMA-ES was paired with HFSS, a commercial computational electromagnetics solver, to optimize the geometry and metasurface characteristics of the A-SBFA, with the goal of maximizing peak directivity at GPS bands L1 (1.575 GHz) and L2 (1.227 GHz)."
  • Wu, L., Wang, X., Wang, G. and Chen, G., 2018. In situ X-ray scattering observation of two-dimensional interfacial colloidal crystallization. Nature Communications, 9(1), p.1335. [ www ] ( CMA-ES | Continuous Optimization )
    • "The data were fitted through the covariance matrix adaptation evolution strategy (CMA-ES) method."
      • "Hansen, N. & Ostermeier, A. Completely derandomized self-adaptation in evolution strategies. Evol. Comput. 9, 159–195 (2001)."
  • Wang, Y., Liu, H., Lv, J., Zhu, L., Wang, H. and Ma, Y., 2011. High pressure partially ionic phase of water ice. Nature Communications, 2(1), p.563. [ www ] ( PSO | Continuous Optimization )
    • "Here we show the pressure-induced formation of a partially ionic phase (monoclinic P21 structure) consisting of coupled alternate layers of (OH)δ− and (H3O)δ+ (δ=0.62) in water ice predicted by particle-swarm optimization structural search at zero temperature and pressures of >14 Mbar."
      • Wang, Y., Lv, J., Zhu, L. & Ma, Y. Crystal structure prediction via particle swarm optimization. Phys. Rev. B 82, 094116 (2010).