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Information-Geometric Optimization Algorithms: A Unifying Picture via Invariance Principles.

, , , and . J. Mach. Learn. Res., (2017)

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Log-Linear Convergence and Divergence of the Scale-Invariant (1+1)-ES in Noisy Environments., , and . Algorithmica, 59 (3): 425-460 (2011)On the adaptation of noise level for stochastic optimization., and . IEEE Congress on Evolutionary Computation, page 3027-3034. IEEE, (2007)Information-Geometric Optimization Algorithms: A Unifying Picture via Invariance Principles., , , and . J. Mach. Learn. Res., (2017)Theoretically Investigating Optimal µ-Distributions for the Hypervolume Indicator: First Results for Three Objectives., , and . PPSN (1), volume 6238 of Lecture Notes in Computer Science, page 586-596. Springer, (2010)Convergence of the Continuous Time Trajectories of Isotropic Evolution Strategies on Monotonic $C^2$ -composite Functions., , and . PPSN (1), volume 7491 of Lecture Notes in Computer Science, page 42-51. Springer, (2012)Theory of Randomized Search Heuristics., and . Algorithmica, 64 (4): 621-622 (2012)COCO: The Bi-objective Black Box Optimization Benchmarking (bbob-biobj) Test Suite., , , and . CoRR, (2016)Dimension-Independent Convergence Rate for Non-isotropic (1, lambda) - ES., , and . GECCO, volume 2723 of Lecture Notes in Computer Science, page 512-524. Springer, (2003)The Impact of Variation Operators on the Performance of SMS-EMOA on the Bi-objective BBOB-2016 Test Suite., , , , , and . GECCO (Companion), page 1225-1232. ACM, (2016)Benchmarking the Pure Random Search on the Bi-objective BBOB-2016 Testbed., , , , , and . GECCO (Companion), page 1217-1223. ACM, (2016)