Applications-of-Evolution-Stratgies-ES.md

Applications of Evolution Stratgies (AppES)

• Rossignolo, M., Reisser, T., Marshall, A., Rembold, P., Pagano, A., Vetter, P.J., Said, R.S., Müller, M.M., Motzoi, F., Calarco, T. and Jelezko, F., 2023. QuOCS: The quantum optimal control suite. Computer Physics Communications, p.108782. [ Qruise ]
• Bonet-Monroig, X., Wang, H., Vermetten, D., Senjean, B., Moussa, C., Bäck, T., Dunjko, V. and O'Brien, T.E., 2023. Performance comparison of optimization methods on variational quantum algorithms. Physical Review A, 107(3), p.032407. [ Google Quantum AI ]
• Li, A.C., Macridin, A., Mrenna, S. and Spentzouris, P., 2023. Simulating scalar field theories on quantum computers with limited resources. Physical Review A, 107(3), p.032603. [ Fermi National Accelerator Laboratory ]
• Böhringer, P., Sommer, D., Haase, T., Barteczko, M., Sprave, J., Stoll, M., Karadogan, C., Koch, D., Middendorf, P. and Liewald, M., 2023. A strategy to train machine learning material models for finite element simulations on data acquirable from physical experiments. Computer Methods in Applied Mechanics and Engineering, 406, p.115894. [ www ] ( Mercedes-Benz AG, Research and Development, Sindelfingen, Germany )
• Arifovic, J., Grimaud, A., Salle, I. and Vermandel, G., 2023. Social learning and monetary policy at the effective lower bound. Available at SSRN 3728108. [ Government of Canada - Bank of Canada + University of Amsterdam ]

2022

• Slade, P., Kochenderfer, M.J., Delp, S.L. and Collins, S.H., 2022. Personalizing exoskeleton assistance while walking in the real world. Nature, 610(7931), pp.277-282. [ Stanford University ]
• De Croon, G.C., Dupeyroux, J.J., De Wagter, C., Chatterjee, A., Olejnik, D.A. and Ruffier, F., 2022. Accommodating unobservability to control flight attitude with optic flow. Nature, 610(7932), pp.485-490.
• Ma, H., Narayanaswamy, A., Riley, P. and Li, L., 2022. Evolving symbolic density functionals. Science Advances, 8(36), p.eabq0279. [ Google Research ]
• Betz, J., Betz, T., Fent, F., Geisslinger, M., Heilmeier, A., Hermansdorfer, L., Herrmann, T., Huch, S., Karle, P., Lienkamp, M. and Lohmann, B., 2022. TUM autonomous motorsport: An autonomous racing software for the Indy Autonomous Challenge. Journal of Field Robotics. [ TUM ]
• Hart, E. and Le Goff, L.K., 2022. Artificial evolution of robot bodies and control: on the interaction between evolution, learning and culture. Philosophical Transactions of the Royal Society B, 377(1843), p.20210117.
• Roy, A.S., Pack, K., Wittler, N. and Machnes, S., 2022. Software tool-set for automated quantum system identification and device bring up. arXiv preprint arXiv:2205.04829. [ Qruise ]
• Yuan, S., Garrison, L.H., Hadzhiyska, B., Bose, S. and Eisenstein, D.J., 2022. ABACUSHOD: A highly efficient extended multitracer HOD framework and its application to BOSS and eBOSS data. Monthly Notices of the Royal Astronomical Society, 510(3), pp.3301-3320. [ Harvard & Smithsonian + Stanford University ]
• Tatsis, K.E., Agathos, K., Chatzi, E.N. and Dertimanis, V.K., 2022. A hierarchical output-only Bayesian approach for online vibration-based crack detection using parametric reduced-order models. Mechanical Systems and Signal Processing, 167, p.108558. [ ETHZ ]
• Wang, B. and Ponce, C.R., 2022. Tuning landscapes of the ventral stream. Cell Reports, 41(6), p.111595. [ Washington University in St. Louis + Harvard Medical School ]

2021

• Vicol, P., Metz, L. and Sohl-Dickstein, J., 2021, July. Unbiased gradient estimation in unrolled computation graphs with persistent evolution strategies. In International Conference on Machine Learning (pp. 10553-10563). PMLR. [ University of Toronto + Google Brain ]
• 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. [ Harvard University ]
• Shir, O.M., Xing, X. and Rabitz, H., 2021. Multi-level evolution strategies for high-resolution black-box control. Journal of Heuristics, 27, pp.1021-1055. [ Princeton University ]
• Pinneri, C., Sawant, S., Blaes, S., Achterhold, J., Stueckler, J., Rolinek, M. and Martius, G., 2021, October. Sample-efficient cross-entropy method for real-time planning. In Conference on Robot Learning (pp. 1049-1065). PMLR. [ Max Planck ETH Center for Learning Systems ]
• Coopmans, L., Luo, D., Kells, G., Clark, B.K. and Carrasquilla, J., 2021. Protocol discovery for the quantum control of majoranas by differentiable programming and natural evolution strategies. PRX Quantum, 2(2), p.020332. [ Vector Institute for Artificial Intelligence ]
• Veit, D., 2021. Genetic algorithms and evolution strategy in textile engineering. In Advances in Modeling and Simulation in Textile Engineering (pp. 99-138). Woodhead Publishing. [ RWTH Aachen University ]
• Fasel, U., Tiso, P., Keidel, D. and Ermanni, P., 2021. Concurrent design and flight mission optimization of morphing airborne wind energy wings. AIAA Journal, 59(4), pp.1254-1268. [ ETHZ ]
• Keidel, D., Fasel, U. and Ermanni, P., 2021. Concept investigation of a lightweight composite lattice morphing wing. AIAA Journal, 59(6), pp.2242-2250. [ ETHZ ]
• Chen, T., 2021. On the interplay between mechanical and computational intelligence in robot hands. Columbia University.
• https://researchspace.auckland.ac.nz/handle/2292/57559

2020

• Witte, K.A., Fiers, P., Sheets-Singer, A.L. and Collins, S.H., 2020. Improving the energy economy of human running with powered and unpowered ankle exoskeleton assistance. Science Robotics, 5(40), p.eaay9108. [ Carnegie Mellon University + Sport Research Lab, Nike Inc. + Stanford University ]

2019

• Büche, D., 2019. Robust compressor optimization by evolutionary algorithms. Uncertainty Management for Robust Industrial Design in Aeronautics: Findings and Best Practice Collected During UMRIDA, a Collaborative Research Project (2013–2016) Funded by the European Union, pp.629-645. [ MAN Diesel & Turbo Schweiz AG ]

2018

• Bujny, M., Aulig, N., Olhofer, M. and Duddeck, F., 2018. Identification of optimal topologies for crashworthiness with the evolutionary level set method. International Journal of Crashworthiness, 23(4), pp.395-416. [ TUM Ph.D. Dissertation ]
• Jȩdrzejewski-Szmek, Z., Abrahao, K.P., Jȩdrzejewska-Szmek, J., Lovinger, D.M. and Blackwell, K.T., 2018. Parameter optimization using covariance matrix adaptation—evolutionary strategy (CMA-ES), an approach to investigate differences in channel properties between neuron subtypes. Frontiers in Neuroinformatics, 12, p.47. [ National Institutes of Health ]

2017

• Padois, V., Ivaldi, S., Babič, J., Mistry, M., Peters, J. and Nori, F., 2017. Whole-body multi-contact motion in humans and humanoids: Advances of the CoDyCo European project. Robotics and Autonomous Systems, 90, pp.97-117. [ INRIA + Max Planck Institute for Intelligent Systems ]
• Batts, Z., Kim, J. and Yamane, K., 2017. Untethered one-legged hopping in 3d using linear elastic actuator in parallel (LEAP). In International Symposium on Experimental Robotics (pp. 103-112). Springer International Publishing. [ Disney ]
• Gehring, C., 2017. Planning and control for agile quadruped robots. Doctoral dissertation, ETH Zurich. [ ETHZ ]
• Song, S., 2017. The development, evaluation and applications of a neuromechanical control model of human locomotion. Doctoral dissertation, Carnegie Mellon University. [ CMU ]
• Alvernaz, S. and Togelius, J., 2017, August. Autoencoder-augmented neuroevolution for visual doom playing. In IEEE Conference on Computational Intelligence and Games (pp. 1-8). IEEE. [ New York University ]

2016

• De Silva, T., Uneri, A., Ketcha, M.D., Reaungamornrat, S., Kleinszig, G., Vogt, S., Aygun, N., Lo, S.F., Wolinsky, J.P. and Siewerdsen, J.H., 2016. 3D–2D image registration for target localization in spine surgery: Investigation of similarity metrics providing robustness to content mismatch. Physics in Medicine & Biology, 61(8), p.3009. [ Johns Hopkins University + Siemens Healthcare XP Division ]
• 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. [ Harvard University + University of Chicago ]

2015

• Song, S., Kim, J. and Yamane, K., 2015, May. Development of a bipedal robot that walks like an animation character. In IEEE International Conference on Robotics and Automation (pp. 3596-3602). IEEE. [ CMU + Disney ]
• Song, S. and Geyer, H., 2015. A neural circuitry that emphasizes spinal feedback generates diverse behaviours of human locomotion. Journal of Physiology, 593(16), pp.3493-3511. [ CMU ]
• Gehring, C., Coros, S., Hutter, M., Bloesch, M., Fankhauser, P., Hoepflinger, M.A. and Siegwart, R., 2014, May. Towards automatic discovery of agile gaits for quadrupedal robots. In IEEE International Conference on Robotics and Automation (pp. 4243-4248). IEEE. [ ETHZ+ Disney ]
• Nigris, D.D., Collins, D.L. and Arbel, T., 2013. Fast rigid registration of pre-operative magnetic resonance images to intra-operative ultrasound for neurosurgery based on high confidence gradient orientations. International Journal of Computer Assisted Radiology and Surgery, 8, pp.649-661.

2012

• 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. [ INRIA ]

2011

• Gazzola, M., Vasilyev, O.V. and Koumoutsakos, P., 2011. Shape optimization for drag reduction in linked bodies using evolution strategies. Computers & structures, 89(11-12), pp.1224-1231. [ ETHZ ]
• Jostins, L. and Jaeger, J., 2010. Reverse engineering a gene network using an asynchronous parallel evolution strategy. BMC Systems Biology, 4, pp.1-16. [ University of Cambridge ]
• Fomekong-Nanfack, Y., Kaandorp, J.A. and Blom, J., 2007. Efficient parameter estimation for spatio-temporal models of pattern formation: Case study of Drosophila melanogaster. Bioinformatics, 23(24), pp.3356-3363.
• Koumoutsakos, P. and Müller, S.D., 2006. Flow optimization using stochastic algorithms. Control of Fluid Flow, pp.213-229.
• Zi, Z. and Klipp, E., 2006. SBML-PET: A systems biology markup language-based parameter estimation tool. Bioinformatics, 22(21), pp.2704-2705.
• Sbalzarini, I.F., Müller, S.D., Koumoutsakos, P.D. and Cottet, G.H., 2001, July. Evolution strategies for computational and experimental fluid dynamic applications. In Proceedings of Annual Conference on Genetic and Evolutionary Computation (pp. 1064-1071). ACM.
• Rechenberg, I., 2000. Case studies in evolutionary experimentation and computation. Computer Methods in Applied Mechanics and Engineering, 186(2-4), pp.125-140.
• Lutz, T. and Wagner, S., 1998. Drag reduction and shape optimization of airship bodies. Journal of Aircraft, 35(3), pp.345-351.
• Bäck, T., Hammel, U., Schütz, M., Schwefel, H.P. and Sprave, J., 1996. Applications of evolutionary algorithms at the center for applied systems analysis. In Computational Methods in Applied Sciences (pp. 243-250).
• Heckler, R. and Schwefel, H.P., 1978. Superimposing direct search methods for parameter optimization onto dynamic simulation models. Institute of Electrical and Electronics Engineers. IEEE.
• Klockgether, J. and Schwefel, H.P., 1970. Two-phase nozzle and hollow core jet experiments. In Engineering Aspects of Magnetohydrodynamics.