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- Timo Kötzing, Hendrik Molter
- PPSN
- 2012

- Jeffrey Heinz, Anna Kasprzik, Timo Kötzing
- Theor. Comput. Sci.
- 2012

Wedefine a collection of language classes which are TxtEx-learnable (learnable in the limit from positive data). The learners map any data input to an element of a fixed lattice, and keep the least upper bound of all lattice elements thus obtained as the current hypothesis. Each element of the lattice is a grammar for a language, and the learner climbs the… (More)

- Christian Gießen, Timo Kötzing
- Algorithmica
- 2014

We consider stochastic versions of OneMax and LeadingOnes and analyze the performance of evolutionary algorithms with and without populations on these problems. It is known that the (1+1) EA on OneMax performs well in the presence of very small noise, but poorly for higher noise levels. We extend these results to LeadingOnes and to many different noise… (More)

- Timo Kötzing, Frank Neumann, Heiko Röglin, Carsten Witt
- ANTS Conference
- 2010

Ant colony optimization (ACO) has been widely used for different combinatorial optimization problems. In this paper, we investigate ACO algorithms with respect to their runtime behavior for the traveling salesperson (TSP) problem. We present a new construction graph and show that it has a stronger local property than the given input graph which is often… (More)

- Dominik D. Freydenberger, Timo Kötzing
- Theory of Computing Systems
- 2013

We study the problem of generalizing from a finite sample to a language taken from a predefined language class. The two language classes we consider are subsets of the regular languages and have significance in the specification of XML documents (the classes corresponding to so called <i>chain regular expressions</i>, Chares, and to <i>single occurrence… (More)

- Florian Benz, Timo Kötzing
- GECCO
- 2013

The smallest grammar problem is the problem of finding the smallest context-free grammar that generates exactly one given sequence. Approximating the problem with a ratio of less than 8569/8568 is known to be NP-hard. Most work on this problem has focused on finding decent solutions fast (mostly in linear time), rather than on good heuristic algorithms.… (More)

- Tobias Friedrich, Timo Kötzing, Martin S. Krejca
- GECCO
- 2016

Estimation of Distribution Algorithms (EDAs) work by iteratively updating a distribution over the search space with the help of samples from each iteration. Up to now, theoretical analyses of EDAs are scarce and present run time results for specific EDAs. We propose a <i>new framework</i> for EDAs that captures the idea of several known optimizers,… (More)

The all-pairs shortest path problem is the first non-artificial problem for which it was shown that adding crossover can significantly speed up a mutation-only evolutionary algorithm. Recently, the analysis of this algorithm was refined and it was shown to have an expected optimization time (w. r. t. the number of fitness evaluations) of Θ(n3.25(log… (More)

- Gilles Bailly, Antti Oulasvirta, Timo Kötzing, Sabrina Hoppe
- UIST
- 2013

Menu systems are challenging to design because design spaces are immense, and several human factors affect user behavior. This paper contributes to the design of menus with the goal of interactively assisting designers with an optimizer in the loop. To reach this goal, 1) we extend a predictive model of user performance to account for expectations as to… (More)

- Benjamin Doerr, Ashish Hota, Timo Kötzing
- GECCO
- 2012

The first rigorous theoretical analysis (Horoba, Sudholt (GECCO 2010)) of an ant colony optimizer for the stochastic shortest path problem suggests that ant system experience significant difficulties when the input data is prone to noise. In this work, we propose a slightly different ant optimizer to deal with noise.
We prove that under mild conditions, it… (More)