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Benchmarking the Pure Random Search on the Bi-objective BBOB-2016 Testbed.

, , , , , and . GECCO (Companion), page 1217-1223. ACM, (2016)

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Identification of the isotherm function in chromatography using CMA-ES., , , , and . IEEE Congress on Evolutionary Computation, page 4289-4296. IEEE, (2007)Convergence results for the (1, lambda)-SA-ES using the theory of phi-irreducible Markov chains.. Theor. Comput. Sci., 334 (1-3): 35-69 (2005)On Proving Linear Convergence of Comparison-based Step-size Adaptive Randomized Search on Scaling-Invariant Functions via Stability of Markov Chains., and . CoRR, (2013)Introduction to Randomized Continuous Optimization., , and . GECCO (Companion), page 333-355. ACM, (2016)GECCO'16 Black-Box Optimization Benchmarking Workshop (BBOB-2016): Workshop Chairs' Welcome Message., , , , , and . GECCO (Companion), page 1167. ACM, (2016)Quantitative Performance Assessment of Multiobjective Optimizers: The Average Runtime Attainment Function., , , and . EMO, volume 10173 of Lecture Notes in Computer Science, page 103-119. Springer, (2017)COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting., , , , and . CoRR, (2016)A new approach to learning in Dynamic Bayesian Networks (DBNs)., , , and . CoRR, (2018)COCO: Performance Assessment., , , , and . CoRR, (2016)COCO: The Experimental Procedure., , , , and . CoRR, (2016)