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Science

  • Schmidt, M. and Lipson, H., 2009. Distilling free-form natural laws from experimental data. Science, 324(5923), pp.81-85. [ eureqa ]

  • Ma, D., Hu, M., Yang, X., Liu, Q., Ye, F., Cai, W., Wang, Y., Xu, X., Chang, S., Wang, R. and Yang, W., 2024. Structural basis for sugar perception by Drosophila gustatory receptors. Science, p.eadj2609. [GA]

    • "Genetic algorithm (GA) was used for the global conformation searching. Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm or Stochastic Steepest Descent (SSD) algorithm was used for local optimization."

  • Higgins, S.I., Conradi, T., Kruger, L.M., O’Hara, R.B. and Slingsby, J.A., 2023. Limited climatic space for alternative ecosystem states in Africa. Science, 380(6649), pp.1038-1042. < DE >

    • "The plant growth model parameters (18 β parameters and an intercept) are estimated from the distribution data using an inverse approach. For this purpose we use a genetic algorithm (DEoptim) that identifies the parameters that maximises the likelihood that the growth model produced the occurrence data. The github repository (https://github.com/pfloek-bt/TTRcodeAESpublic, DOI: 10.5281/zenodo.7854825) provides the R code and a tutorial for fitting the species level growth models."
      • R. Storn, K. Price, Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11, 341–359 (1997). doi:10.1023/A:1008202821328
      • D. Ardia, K. Boudt, P. Carl, K. M. Mullen, B. G. Peterson, Differential evolution with DEoptim. An application to non-convex portfolio optimization. R J. 3, 27–34 (2011). doi:10.32614/RJ-2011-005

  • Yang, Q., Hu, J., Fang, Y.W., Jia, Y., Yang, R., Deng, S., Lu, Y., Dieguez, O., Fan, L., Zheng, D. and Zhang, X., 2023. Ferroelectricity in layered bismuth oxide down to 1 nanometer. Science, 379(6638), pp.1218-1224. [ www ] ( GA + Discrete Optimization #) [ 北京科技大学 ]

    • "Hence, we performed genetic-algorithm searches of the arrangements that lead to the lowest possible energy of each set of atoms in these cells of Bi6On (n = 6 to 12) with different oxygen loss."
    • "To further understand the atomic structure, we performed a high-throughput DFT crystal structure prediction within the structure space of Bi6O9 that combined the genetic-algorithm implemented in USPEX (Universal Structure Predictor: Evolutionary Xtallography) with information from the HAADF-STEM structures."
      • C. W. Glass, A. R. Oganov, N. Hansen, USPEX—Evolutionary crystal structure prediction. Comput. Phys. Commun.175, 713–720 (2006).
      • A. O. Lyakhov, A. R. Oganov, H. T. Stokes, Q. Zhu, New developments in evolutionary structure prediction algorithm USPEX. Comput. Phys. Commun.184, 1172–1182 (2013).
    • "XRR curves were fit using GenX software. The crestal truncation rods (CTRs) were fit using the GenX genetic based x-ray fitting algorithm to determine the structural properties of the films."
      • "M. Björck, G. Andersson, GenX: An extensible x-ray reflectivity refinement program utilizing differential evolution. J. Appl. Cryst. 40, 1174–1178 (2007)."

  • Xiong, B., Liu, Y., Xu, Y., Deng, L., Chen, C.W., Wang, J.N., Peng, R., Lai, Y., Liu, Y. and Wang, M., 2023. Breaking the limitation of polarization multiplexing in optical metasurfaces with engineered noise. Science, 379(6629), pp.294-299. [ www ] ( GA ) [ 南京大学 ]

    • "The final approximate solutions with engineered correlated and noncorrelated noise are fed into the genetic algorithm as complex numbers, which can search the optimized geometrical parameters for each pixel with the smallest mean square error. The genetic algorithm is critical to the design of the metasurface. By mimicking the natural processes of selection, reproduction, mutation, and crossover, the genetic algorithm is a metaheuristic algorithm that can solve global optimization problems and effectively explore the parameter space."

  • Li, S., Driver, T., Rosenberger, P., Champenois, E.G., Duris, J., Al-Haddad, A., Averbukh, V., Barnard, J.C., Berrah, N., Bostedt, C. and Bucksbaum, P.H., 2022. Attosecond coherent electron motion in Auger-Meitner decay. Science, 375(6578), pp.285-290. [ www ] ( DE )

    • "The genetic optimization algorithm optimizes the position of this ring, to maximize the integrated image density which falls within the ring. The deviation of the center of the ring from the detector origin at this optimized position is determined to be the momentum shift induced by the streaking field."
      • R. Storn, K. Price, J. Glob. Optim. 11, 341–359 (1997). doi:10.1023/A:1008202821328

  • Gassler, J., Kobayashi, W., Gáspár, I., Ruangroengkulrith, S., Mohanan, A., Gómez Hernández, L., Kravchenko, P., Kümmecke, M., Lalic, A., Rifel, N. and Ashburn, R.J., 2022. Zygotic genome activation by the totipotency pioneer factor Nr5a2. Science, 378(6626), pp.1305-1315.

  • Nathan, R., Monk, C.T., Arlinghaus, R., Adam, T., Alós, J., Assaf, M., Baktoft, H., Beardsworth, C.E., Bertram, M.G., Bijleveld, A.I. and Brodin, T., 2022. Big-data approaches lead to an increased understanding of the ecology of animal movement. Science, 375(6582), p.eabg1780.

  • Ruano-Gallego, D., Sanchez-Garrido, J., Kozik, Z., Núñez-Berrueco, E., Cepeda-Molero, M., Mullineaux-Sanders, C., Naemi Baghshomali, Y., Slater, S.L., Wagner, N., Glegola-Madejska, I. and Roumeliotis, T.I., 2021. Type III secretion system effectors form robust and flexible intracellular virulence networks. Science, 371(6534), p.eabc9531. [ www ] ( GA )

    • "To adjust these weights, we developed a methodology combining artificial neural networks with a genetic algorithm."
    • "Training consists of assigning quantitative weight to every connection via a genetic algorithm."
    • "The weights were adjusted applying a real-coded steady-state multipopulation genetic algorithm."
      • D. Barrios, A. Carrascal, D. Manrique, J. Ríos, Optimisation With Real-Coded Genetic Algorithms Based On Mathematical Morphology. Int. J. Comput. Math. 80, 275–293 (2003).

  • Fan, J.X., Shen, S.Z., Erwin, D.H., Sadler, P.M., MacLeod, N., Cheng, Q.M., Hou, X.D., Yang, J., Wang, X.D., Wang, Y. and Zhang, H., 2020. A high-resolution summary of Cambrian to Early Triassic marine invertebrate biodiversity. Science, 367(6475), pp.272-277. [ www ] ( GA+SA | Parallel ) [ 南京大学 ]

  • Mary, A., Dayan, J., Leone, G., Postel, C., Fraisse, F., Malle, C., Vallée, T., Klein-Peschanski, C., Viader, F., De la Sayette, V. and Peschanski, D., 2020. Resilience after trauma: The role of memory suppression. Science, 367(6479), p.eaay8477.

  • Zhang, J., Fiers, P., Witte, K.A., Jackson, R.W., Poggensee, K.L., Atkeson, C.G. and Collins, S.H., 2017. Human-in-the-loop optimization of exoskeleton assistance during walking. Science, 356(6344), pp.1280-1284. { CMA-ES }

  • Villeneuve, D.M., Hockett, P., Vrakking, M.J.J. and Niikura, H., 2017. Coherent imaging of an attosecond electron wave packet. Science, 356(6343), pp.1150-1153. [ www ] ( PSO + Continuous Optimization #)

    • "To ensure that a global optimum was found, we employed a particle swarm optimization algorithm."
      • J. Kennedy, R. Eberhart, “Particle swarm optimization,” in the Institute of Electrical and Electronics Engineers (IEEE) International Conference on Neural Networks Proceedings (IEEE, 1995), vol. 4, pp. 1942–1948.

  • Mannix, A.J., Zhou, X.F., Kiraly, B., Wood, J.D., Alducin, D., Myers, B.D., Liu, X., Fisher, B.L., Santiago, U., Guest, J.R. and Yacaman, M.J., 2015. Synthesis of borophenes: Anisotropic, two-dimensional boron polymorphs. Science, 350(6267), pp.1513-1516. [ www ] ( GA + Discrete Optimization #)

    • "These experimental results are further elucidated by ab initio evolutionary structure prediction with the USPEX algorithm, which minimizes the thermodynamic potential of the system using density functional theory (DFT)."
      • A. R. Oganov, C. W. Glass, J. Chem. Phys. 124, 244704–244716 (2006).
      • Q. Zhu, L. Li, A. R. Oganov, P. B. Allen, Phys. Rev. B 87, 195317 (2013).

  • Zhang, W., Oganov, A.R., Goncharov, A.F., Zhu, Q., Boulfelfel, S.E., Lyakhov, A.O., Stavrou, E., Somayazulu, M., Prakapenka, V.B. and Konôpková, Z., 2013. Unexpected stable stoichiometries of sodium chlorides. Science, 342(6165), pp.1502-1505. [ www | www ] ( GA + Discrete Optimization #)

    • "To find stable Na-Cl compounds and their structures that may not have been possible to observe experimentally or computationally, we used the ab initio evolutionary algorithm USPEX, which can simultaneously find stable stoichiometries and the corresponding structures in multicomponent systems."
      • A. R. Oganov, C. W. Glass, J. Chem. Phys. 124, 244704 (2006).
      • A. O. Lyakhov, A. R. Oganov, H. T. Stokes, Q. Zhu, Comput. Phys. Commun. 184, 1172–1182 (2013).
      • A. R. Oganov, A. O. Lyakhov, M. Valle, Acc. Chem. Res. 44, 227–237 (2011).
      • A. R. Oganov, Y. Ma, A. O. Lyakhov, M. Valle, C. Gatti, Rev. Mineral. Geochem. 71, 271–298 (2010).

  • Pfeifer, R., Lungarella, M. and Iida, F., 2007. Self-organization, embodiment, and biologically inspired robotics. Science, 318(5853), pp.1088-1093. [ www ]

  • Bongard, J., Zykov, V. and Lipson, H., 2006. Resilient machines through continuous self-modeling. Science, 314(5802), pp.1118-1121. [ www ] ( ER )

  • Assion, A., Baumert, T., Bergt, M., Brixner, T., Kiefer, B., Seyfried, V., Strehle, M. and Gerber, G., 1998. Control of chemical reactions by feedback-optimized phase-shaped femtosecond laser pulses. Science, 282(5390), pp.919-922. [ www ] ( GA | ES )

    • D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning (Addison-Wesley, Reading, UK, 1993);
    • H.-P. Schwefel, Evolution and Optimum Seeking (Wiley, New York, 1995).

  • Macready, W.G., Siapas, A.G. and Kauffman, S.A., 1996. Criticality and parallelism in combinatorial optimization. Science, 271(5245), pp.56-59. [ www ]

  • Cvijović, D. and Klinowski, J., 1995. Taboo search: An approach to the multiple minima problem. Science, 267(5198), pp.664-666. [ www ] ( TS | Continuous Optimization )

  • Forrest, S., 1993. Genetic algorithms: Principles of natural selection applied to computation. Science, 261(5123), pp.872-878. [ www ] ( GA )

  • Kirkpatrick, S., Gelatt, C.D. and Vecchi, M.P., 1983. Optimization by simulated annealing. Science, 220(4598), pp.671-680. [ www ] ( SA )

  • Aguilar, J., Monaenkova, D., Linevich, V., Savoie, W., Dutta, B., Kuan, H.S., Betterton, M.D., Goodisman, M.A.D. and Goldman, D.I., 2018. Collective clog control: Optimizing traffic flow in confined biological and robophysical excavation. Science, 361(6403), pp.672-677. [ www ]

  • Hornby, G.S. and Kurtoglu, T., 2009. Toward a smarter Web. Science, 325(5938), pp.277-278. [ www ]