T. Friedrich

Publications274
h-index38
Citations4,625
Highly Influential Citations269
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Approximating the volume of unions and intersections of high-dimensional geometric objects
Why rumors spread so quickly in social networks
TLDR
A few hubs with many connections share with many individuals with few connections, leading to a chain of relationships that is mutually beneficial to both parties.
Approximation-Guided Evolutionary Multi-Objective Optimization
TLDR
This work presents a new framework of an evolutionary algorithm for multi-objective optimization that allows to work with a formal notion of approximation and shows that this approach outperforms state-of-the-art evolutionary algorithms in terms of the quality of the approximation that is obtained.
Social networks spread rumors in sublogarithmic time
TLDR
This work studies the performance of randomized rumor spreading protocols on graphs in the preferential attachment model and proves the first time that a sublogarithmic broadcast time is proven for a natural setting.
Maximizing Submodular Functions under Matroid Constraints by Evolutionary Algorithms
TLDR
This paper investigates the runtime of a simple single objective evolutionary algorithm called () EA and a multiobjective evolutionary algorithms called GSEMO until they have obtained a good approximation for submodular functions.
The Compact Genetic Algorithm is Efficient Under Extreme Gaussian Noise
TLDR
The concept of graceful scaling is introduced in which the run time of an algorithm scales polynomially with noise intensity, and it is shown that a simple EDA called the compact genetic algorithm can overcome the shortsightedness of mutation-only heuristics to scale gracefully with noise.
Approximating the Volume of Unions and Intersections of High-Dimensional Geometric Objects
TLDR
The algorithm allows to approximate efficiently the volume of the union of convex bodies given by weak membership oracles, and proves #P-hardness for boxes and shows that there is no multiplicative polynomial-time 2d1-z-approximation for certain boxes unless NP=BPP.
Do additional objectives make a problem harder?
TLDR
This paper examines how adding objectives to a given optimization problem affects the computation effort required to generate the set of Pareto-optimal solutions and shows that additional objectives may be both beneficial and obstructive depending on the chosen objective.
Approximating covering problems by randomized search heuristics using multi-objective models
TLDR
It is shown that optimal solutions can be approximated within a factor of log n using the multi-objective approach while the approximation quality obtainable by the single- objective approach in expected polynomial time may be arbitrarily bad.
Approximating Covering Problems by Randomized Search Heuristics Using Multi-Objective Models
TLDR
It is shown that optimal solutions can be approximated within a logarithmic factor of the size of the ground set, using the multi-objective approach, while the approximation quality obtainable by the single- objective approach in expected polynomial time may be arbitrarily bad.
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