Michael Gnewuch

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—Motivated by neutrality observed in natural evolution often redundant encodings are used in evolutionary algorithms. Many experimental studies have been carried out on this topic. In this paper we present a first rigorous runtime analysis on the effect of using neutrality. We consider a simple model where a layer of constant fitness is distributed in the(More)
For numerical integration in higher dimensions, bounds for the star-discrepancy with polynomial dependence on the dimension d are desirable. Furthermore , it is still a great challenge to give construction methods for low-discrepancy point sets. In this paper we give upper bounds for the star-discrepancy and its inverse for subsets of the d-dimensional unit(More)
In memory of our friend, colleague and former fellow student Manfred Schocker Summary. We provide a deterministic algorithm that constructs small point sets exhibiting a low star discrepancy. The algorithm is based on bracketing and on recent results on randomized roundings respecting hard constraints. It is structurally much simpler than the previous(More)
We investigate the problem of constructing small point sets with low star discrepancy in the s-dimensional unit cube. The size of the point set shall always be polynomial in the dimension s. Our particular focus is on extending the dimension of a given low-discrepancy point set. This results in a deterministic algorithm that constructs N-point sets with(More)
The extreme or unanchored discrepancy is the geometric discrepancy of point sets in the d-dimensional unit cube with respect to the set system of axis-parallel boxes. For 2 ≤ p < ∞ we provide upper bounds for the average L p-extreme discrepancy. With these bounds we are able to derive upper bounds for the inverse of the L ∞-extreme discrepancy with optimal(More)