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TOG (ACM Transactions on Graphics)

  • Lee, S., Lee, J. and Lee, J., 2022. Learning virtual chimeras by dynamic motion reassembly. ACM Transactions on Graphics, 41(6), pp.1-13. [ www ] ( CMA-ES + Continuous Optimization #)
    • "We use a derivative-free optimization method, CMA-ES, to solve this optimization problem [Hansen and Ostermeier 1996]."
      • N. Hansen and A. Ostermeier. 1996. Adapting arbitrary normal mutation distributions in evolution strategies: the covariance matrix adaptation. In Proceedings of IEEE International Conference on Evolutionary Computation. 312–317.
  • Edelstein, M., Ezuz, D. and Ben-Chen, M., 2020. ENIGMA: Evolutionary non-isometric geometry matching. ACM Transactions on Graphics, 39(4), pp.112-1. [ www | pdf ] (GA)
  • Cucerca, S., Didyk, P., Seidel, H.P. and Babaei, V., 2020. Computational image marking on metals via laser induced heating. ACM Transactions on Graphics, 39(4), pp.70-1. [ www | pdf ]
  • Wu, C., Zhao, H., Nandi, C., Lipton, J.I., Tatlock, Z. and Schulz, A., 2019. Carpentry compiler. ACM Transactions on Graphics, 38(6), pp.1-14. [ www | C++ | project ]
  • Sahillioğlu, Y., 2018. A genetic isometric shape correspondence algorithm with adaptive sampling. ACM Transactions on Graphics, 37(5), pp.1-14. [ www ]
  • Lee, Y., Lee, K., Kwon, S.S., Jeong, J., O'Sullivan, C., Park, M.S. and Lee, J., 2015. Push-recovery stability of biped locomotion. ACM Transactions on Graphics, 34(6), pp.1-9.
    • "A Covariance Matrix Adaptation (CMA) strategy is employed to search for optimal parameter values given the highly-nonlinear objective function. This process takes approximately 2.5 hours using 12 Intel Xeon X5670 2.93GHz CPUs with population size 50 and maximum iteration 600."
      • HANSEN, N. 2006. The CMA evolution strategy: A comparing review. In Towards a New Evolutionary Computation, vol. 192 of Studies in Fuzziness and Soft Computing. 75–102.
  • Tompson, J., Stein, M., Lecun, Y. and Perlin, K., 2014. Real-time continuous pose recovery of human hands using convolutional networks. ACM Transactions on Graphics, 33(5), pp.1-10. [ www ] ( PSO | Continuous Optimization )
  • Geijtenbeek, T., Van De Panne, M. and Van Der Stappen, A.F., 2013. Flexible muscle-based locomotion for bipedal creatures. ACM Transactions on Graphics, 32(6), pp.1-11. [ www ] ( CMA-ES + Continuous Optimization #)
    • "The total set of parameters is optimized using Covariance Matrix Adaptation [Hansen 2006]."
      • "HANSEN, N. 2006. The CMA evolution strategy: a comparing review. Towards a new evolutionary computation, 75–102."
  • Stoll, C., Gall, J., De Aguiar, E., Thrun, S. and Theobalt, C., 2010. Video-based reconstruction of animatable human characters. ACM Transactions on Graphics, 29(6), pp.1-10. [ www ] ( CMA-ES + Continuous Optimization #)
    • "To tackle the challenging energy functional, we apply the evolution strategy with covariance matrix adaption (CMA-ES) [Hansen et al. 2009] to find a minimum of the energy function. In the following we briefly describe its core concepts and refer the reader to the original paper for a more in-depth discussion."
      • HANSEN, N., NIEDERBERGER, A. S. P., GUZZELLA, L., AND KOUMOUTSAKOS, P. 2009. A method for handling uncertainty in evolutionary optimization with an application to feedback control of combustion. IEEE TEC.
  • Wang, J.M., Fleet, D.J. and Hertzmann, A., 2009. Optimizing walking controllers. ACM Transactions on Graphics (pp. 1-8). [ www ] ( CMA-ES + Continuous Optimization #)
    • "The optimization problem is high-dimensional, discontinuous, and subject to many local minima. Moreover, each function evaluation involves a simulation in ODE, which runs in approximately real-time. It is important for the method to use as few function evaluations as possible, without the need to evaluate gradients. We tested several different optimization algorithms, and found Covariance Matrix Adaptation (CMA) [Hansen 2006] to work best."
      • HANSEN, N. 2006. The CMA evolution strategy: A comparing review. In Towards a New Evolutionary Computation. Advances on Estimation of Distribution Algorithms. Springer, 75–102.
  • Wampler, K. and Popović, Z., 2009. Optimal gait and form for animal locomotion. ACM Transactions on Graphics, 28(3), pp.1-8. [ www ] ( CMA-ES + Continuous Optimization #)
    • "In order to solve the gait optimization problem we use a novel hybrid optimization technique which combines a spacetime optimization as an inner loop to a sampling-based derivative-free optimization method based on a variant of the covariance matrix adaptation evolution strategy (CMA). This combines the efficiency in high dimensional spaces and ability to handle general constraints of spacetime optimization with the ability to handle non-differentiable variables and avoid many local minima."
      • HANSEN, N., AND KERN, S. 2004. Evaluating the CMA evolution strategy on multimodal test functions. In Parallel Problem Solving from Nature PPSN VIII, Springer, X. Yao et al., Eds., vol. 3242 of LNCS, 282–291.
      • HANSEN, N., HANSEN, N., OSTERMEIER, A., AND OSTERMEIER, A. 1996. Adapting arbitrary normal mutation distributions in evolution strategies: the covariance matrix adaptation. Morgan Kaufmann, 312–317.