Sibylle D. Müller

TL;DR: A novel evolutionary optimization strategy based on the derandomized evolution strategy with covariance matrix adaptation (CMA-ES), intended to reduce the number of generations required for convergence to the optimum, which results in a highly parallel algorithm which scales favorably with large numbers of processors.

TL;DR: A unifying formulation is presented for five Evolutionary Algorithms that generate new population members by sampling a probability distribution constructed during the optimization process to characterize them based on the parametrization of the probability distribution, the learning methodology, and the use of historical information.

TL;DR: This work considers the application of reinforcement learning for determining the relationship between success rates and the adaptation of step sizes in the (1+1)-evolution strategy and indicates that assigning good reward measures seems to be crucial to the performance of the combined strategy.

TL;DR: In this paper, the authors conduct a numerical study of mixing in a transverse momentum micromixer and find that the optimal frequencies for increasing number of transverse channels are superposable despite the non-linear nature of the mixing process.

TL;DR: In this article, the effect of the size of the particles in a wall jet on the velocity of the wall jet has been investigated and compared to the effects of the number of particles in the wall.