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PNAS (Proceedings of the National Academy of Sciences)

  • Sherman, D. and Harel, D., 2024. Deciphering the underlying mechanisms of the pharyngeal pumping motions in Caenorhabditis elegans. Proceedings of the National Academy of Sciences, 121(7), p.e2302660121. [GA]
  • Rosano, G., Barzasi, A. and Lynagh, T., 2024. Loss of activation by GABA in vertebrate delta ionotropic glutamate receptors. Proceedings of the National Academy of Sciences, 121(6), p.e2313853121. [GA]
    • "Employing a blind docking approach, 100 experiments were carried out for each ligand, with a maximum of 25 million energy evaluations per experiment using the Lamarckian genetic algorithm and default parameters from AutoDock4.2."
  • Zhang, Z., Kummeth, A.L., Yang, J.Y. and Alexandrova, A.N., 2022. Inverse molecular design of alkoxides and phenoxides for aqueous direct air capture of CO2. Proceedings of the National Academy of Sciences, 119(25), p.e2123496119. [GA]
    • "We apply a genetic algorithm to search the chemical space of substituted phenoxides for the optimal sorbent."
  • Bayer, A.D., Lautenbach, S. and Arneth, A., 2023. Benefits and trade-offs of optimizing global land use for food, water, and carbon. Proceedings of the National Academy of Sciences, 120(42), p.e2220371120. ( NSGA-II )
  • https://www.pnas.org/doi/10.1073/pnas.2221913120
  • Mueller, K.N., Carter, M.C., Kansupada, J.A. and Ponce, C.R., 2023. Macaques recognize features in synthetic images derived from ventral stream neurons. Proceedings of the National Academy of Sciences, 120(10), p.e2213034120. [ www ] ( EC + Continuous Optimization #)
    • "We established that generators can be functionally linked with in vivo neurons in a closed-loop approach—generated images are displayed in the receptive field, neuronal responses are collected, and an evolutionary search algorithm (30) provides generators with new input codes likely associated with images that will increase neuronal firing rates"
      • Y. Yamane, E. T. Carlson, K. C. Bowman, Z. Wang, C. E. Connor, A neural code for three-dimensional object shape in macaque inferotemporal cortex. Nat. Neurosci. 11, 1352–1360 (2008).
      • C. R. Ponce et al., Evolving images for visual neurons using a deep generative network reveals coding principles and neuronal preferences. Cell 177, 999–1009.e10 (2019), 10.1016/j. cell.2019.04.005.
      • O. Rose, J. Johnson, B. Wang, C. R. Ponce, Visual prototypes in the ventral stream are attuned to complexity and gaze behavior. Nat. Commun. 12, 1–16 (2021).
  • Falk, M.J., Wu, J., Matthews, A., Sachdeva, V., Pashine, N., Gardel, M.L., Nagel, S.R. and Murugan, A., 2023. Learning to learn by using nonequilibrium training protocols for adaptable materials. Proceedings of the National Academy of Sciences, 120(27), p.e2219558120. { CMA-ES }
    • "We optimize the yield of a given target structure over the 66 design parameters using the covariance matrix adaptation evolutionary strategy (CMA-ES) that simulates an evolving population of design parameters."
      • N. Hansen, A. Ostermeier, Adapting arbitrary normal mutation distributions in evolution strategies: The covariance matrix adaptation in Proceedings of IEEE international conference on evolutionary computation. (IEEE), pp. 312–317 (1996).
      • N. Hansen, The cma evolution strategy: A comparing review. Towards New Evol. Comput. pp.75–102 (2006).
  • Garg, S., Shiragur, K., Gordon, D.M. and Charikar, M., 2023. Distributed algorithms from arboreal ants for the shortest path problem. Proceedings of the National Academy of Sciences, 120(6), p.e2207959120. [ www ] ( SI | Distributed )
  • Wang, G., Phan, T.V., Li, S., Wang, J., Peng, Y., Chen, G., Qu, J., Goldman, D.I., Levin, S.A., Pienta, K. and Amend, S., 2022. Robots as models of evolving systems. Proceedings of the National Academy of Sciences, 119(12), p.e2120019119. [ www | pdf ] ( ER | SR )
  • Shankar, S., Raju, V. and Mahadevan, L., 2022. Optimal transport and control of active drops. Proceedings of the National Academy of Sciences, 119(35), p.e2121985119. [ www ] ( CMA-ES | Continuous Optimization )
    • "We implement this using a gradient-free covariance matrix adaptation evolution strategy (CMA-ES). [constrained numerical optimization of the nonlinear continuum PDE using a gradient-free evolutionary algorithm, such as CMA-ES]"
      • N Hansen, Y Akimoto, P Baudis, CMA-ES/pycma on Github (Zenodo, DOI:10.5281/zenodo.2559634) (2019).
      • N. Hansen, “The CMA evolution strategy: A comparing review” in Towards a New Evolutionary Computation, J. A. Lozano, P. Larrañaga, I. Inza, E. Bengoetxea, Eds. (Studies in Fuzziness and Soft Computing, Springer Berlin Heidelberg, Berlin), vol. 192, pp. 75–102 (2006).
  • Zhou, J., Wang, T., Chen, L., Liao, L., Wang, Y., Xi, S., Chen, B., Lin, T., Zhang, Q., Ye, C. and Zhou, X., 2022. Boosting the reaction kinetics in aprotic lithium-carbon dioxide batteries with unconventional phase metal nanomaterials. Proceedings of the National Academy of Sciences, 119(40), p.e2204666119. [ www | pdf ] ( PSO | Continuous Optimization )
    • "In the first place, phonon spectra were simulated to verify the dynamic stability of the 4H Ir crystal obtained by the CALYPSO."
      • Y. Wang, J. Lv, L. Zhu, Y. Ma, CALYPSO: A method for crystal structure prediction. Comput. Phys. Commun. 183, 2063–2070 (2012).
  • Xia, W., Sakurai, M., Balasubramanian, B., Liao, T., Wang, R., Zhang, C., Sun, H., Ho, K.M., Chelikowsky, J.R., Sellmyer, D.J. and Wang, C.Z., 2022. Accelerating the discovery of novel magnetic materials using machine learning–guided adaptive feedback. Proceedings of the National Academy of Sciences, 119(47), p.e2204485119. [ www ] ( GA #)
    • "Here, we report our discovery and synthesis of an RE-free magnetic compound, Fe3CoB2, through an efficient feedback framework by integrating machine learning (ML), an adaptive genetic algorithm, first-principles calculations, and experimental synthesis."
  • Wang, L., Zheng, D., Harker, P., Patel, A.B., Guo, C.F. and Zhao, X., 2021. Evolutionary design of magnetic soft continuum robots. Proceedings of the National Academy of Sciences, 118(21), p.e2021922118. [ www ] ( GA #)
    • "The proposed MSCR design is enabled by integrating a theoretical model and the genetic algorithm."
  • Kriegman, S., Blackiston, D., Levin, M. and Bongard, J., 2020. A scalable pipeline for designing reconfigurable organisms. Proceedings of the National Academy of Sciences, 117(4), pp.1853-1859. [ www | pdf | Python | cdorgs ] ( ER )
  • An, S., Cho, S.Y., Kang, J., Lee, S., Kim, H.S., Min, D.J., Son, E. and Cho, K.H., 2020. Inhibition of 3-phosphoinositide–dependent protein kinase 1 (PDK1) can revert cellular senescence in human dermal fibroblasts. Proceedings of the National Academy of Sciences, 117(49), pp.31535-31546. [ www | pdf ] ( DE )
  • Qin, S., Li, Q., Tang, C. and Tu, Y., 2019. Optimal compressed sensing strategies for an array of nonlinear olfactory receptor neurons with and without spontaneous activity. Proceedings of the National Academy of Sciences, 116(41), pp.20286-20295. [ www ] ( CMA-ES | Continuous Optimization )
    • "We use the CMA-ES algorithm to search the optimal sensitivity matrix. At each iteration, a population of candidate sensitivity matrices are sampled and their performance are estimated. The subpopulation that have better performance then determines the next generation of candidates. The iteration keeps going until the solution converges. For each parameter set, we perform many simulations from random starting points."
      • N. Hansen, A. Ostermeier, Completely derandomized self-adaptation in evolution strategies. Evol. Comput. 9, 159–195 (2001).
      • N. Hansen, “The cma evolution strategy: A comparing review” in Towards a New Evolutionary Computation: Advances on Estimation of Distribution Algorithms, J. A. Lozano, P. Larrañaga, E. Bengoetxea, Eds. (Springer, 2006), pp. 75–102.
  • Turner, A.J., Frankenberg, C., Wennberg, P.O. and Jacob, D.J., 2017. Ambiguity in the causes for decadal trends in atmospheric methane and hydroxyl. Proceedings of the National Academy of Sciences, 114(21), pp.5367-5372. [ www ] ( CMA-ES + Continuous Optimization #)
    • "We use the covariance matrix adaptation evolution strategy (CMA-ES) to find the most likely solution. Typical sampling methods [such as Markov chain Monte Carlo (MCMC)] become prohibitively slow as the dimension of the state vector becomes large because they have trouble defining the proposal distribution. CMA-ES is an evolutionary algorithm that modifies the covariance matrix of the proposal distribution based on the fitness of multiple candidate solutions in a given generation. This allows CMA-ES to efficiently sample the posterior distribution. We restart CMA-ES with 10 different initializations and covariance matrices in an attempt to find a global minimum. In total, we draw 500,000,000 samples from the posterior distribution."
      • N Hansen, The CMA Evolution Strategy: A comparing review. Towards a New Evolutionary Computation. Advances on Estimation of Distribution Algorithms, eds JA Lozano, P Larraaga, I Inza, E Bengoetxea (Springer, Berlin), pp. 75–102 (2006).
  • Zwicker, D., Murugan, A. and Brenner, M.P., 2016. Receptor arrays optimized for natural odor statistics. Proceedings of the National Academy of Sciences, 113(20), pp.5570-5575. [ www ] ( CMA-ES | Continuous Optimization )
    • "Consequently, we use the stochastic, derivative-free numerical optimization method covariance matrix adaptation evolution strategy (CMA-ES) to optimize the sensitivity matrix."
      • N Hansen, The CMA evolution strategy: A comparing review. Towards a New Evolutionary Computation, eds JA Lozano, P Larrañaga, I Inza, E Bengoetxea (Springer, New York), pp. 75–102 (2006).
  • Srinivasan, B., Vo, T., Zhang, Y., Gang, O., Kumar, S. and Venkatasubramanian, V., 2013. Designing DNA-grafted particles that self-assemble into desired crystalline structures using the genetic algorithm. Proceedings of the National Academy of Sciences, 110(46), pp.18431-18435. [GA]
  • Wang, H., Tse, J.S., Tanaka, K., Iitaka, T. and Ma, Y., 2012. Superconductive sodalite-like clathrate calcium hydride at high pressures. Proceedings of the National Academy of Sciences, 109(17), pp.6463-6466. [ www | pdf ] ( PSO | Continuous Optimization )
    • "Our structure prediction approach is based on a global minimization of freeenergy surfaces merging ab initio total-energy calculations via particle swarmoptimization technique as implemented in CALYPSO (crystal structure analy-sis by particle swarm optimization) code."
  • Wischmann, S., Floreano, D. and Keller, L., 2012. Historical contingency affects signaling strategies and competitive abilities in evolving populations of simulated robots. Proceedings of the National Academy of Sciences, 109(3), pp.864-868. [ www | pdf ]
  • Uhlendorf, J., Miermont, A., Delaveau, T., Charvin, G., Fages, F., Bottani, S., Batt, G. and Hersen, P., 2012. Long-term model predictive control of gene expression at the population and single-cell levels. Proceedings of the National Academy of Sciences, 109(35), pp.14271-14276. [ www ] ( CMA-ES | Continuous Optimization )
  • Zhu, L., Wang, Z., Wang, Y., Zou, G., Mao, H.K. and Ma, Y., 2012. Spiral chain O4 form of dense oxygen. Proceedings of the National Academy of Sciences, 109(3), pp.751-753. [ www | pdf ] ( PSO | Continuous Optimization )
    • "We report here the prediction of the dissociation ofmolecular oxygen into a polymeric spiral chain O4structure (spacegroupI41∕acd,θ-O4) above 1.92-TPa pressure using the particle-swarm search method."
  • Bongard, J., 2011. Morphological change in machines accelerates the evolution of robust behavior. Proceedings of the National Academy of Sciences, 108(4), pp.1234-1239. [ www | pdf ]
  • Hwang, D., Rust, A.G., Ramsey, S., Smith, J.J., Leslie, D.M., Weston, A.D., De Atauri, P., Aitchison, J.D., Hood, L., Siegel, A.F. and Bolouri, H., 2005. A data integration methodology for systems biology. Proceedings of the National Academy of Sciences, 102(48), pp.17296-17301. [ www | pdf ] ( SA )
  • Lemmon, A.R. and Milinkovitch, M.C., 2002. The metapopulation genetic algorithm: An efficient solution for the problem of large phylogeny estimation. Proceedings of the National Academy of Sciences, 99(16), pp.10516-10521. [GA + ME]
  • Vanchurin, V., Wolf, Y.I., Katsnelson, M.I. and Koonin, E.V., 2022. Toward a theory of evolution as multilevel learning. Proceedings of the National Academy of Sciences, 119(6), p.e2120037119. [ www | pdf ]