5 citations · 9 across the 12 of their papers we have counts for
15 papers
Combinatorial Landscape Analysis for Dominating Set and Vertex Coloring
Johanna Gasse, Antonia Heinen, Felix Knöfel +2
We analyze the two combinatorial problems of Dominating Set and Vertex Coloring regarding what kind of local optima are present for various instances. For a variety of graph classe…
Gray-Box Optimization and the Vertex Coloring Problem
Johanna Gasse, Antonia Heinen, Hendrik Higl +1
Gray-box optimization is an approach for making some problem-specific information available to the algorithm while still relying on fitness information as the main guide to an opti…
Analysis of Search Heuristics in the Multi-Armed Bandit Setting
Jasmin Brandt, Barbara Hammer, Timo Kötzing +1
We consider the classic Multi-Armed Bandit setting to understand the exploration/exploitation tradeoffs made by different search heuristics. Since many search heuristics work by co…
Anytime Analysis on BinVal: Adaptive Parameters Help
Timo Kötzing, Jurek Sander
While most theoretical run time analyses of discrete randomized search heuristics provide bounds on the expected number of evaluations to find the global optimum, we consider the a…
Run Time Bounds for Integer-Valued OneMax Functions
Jonathan Gadea Harder, Timo Kötzing, Xiaoyue Li +1
While most theoretical run time analyses of discrete randomized search heuristics focused on finite search spaces, we consider the search space . This is a further ge…
Analysis of the (1+1) EA on LeadingOnes with Constraints
Tobias Friedrich, Timo Kötzing, Aneta Neumann +2
Understanding how evolutionary algorithms perform on constrained problems has gained increasing attention in recent years. In this paper, we study how evolutionary algorithms optim…
ELEA -- Build your own Evolutionary Algorithm in your Browser
Markus Wagner, Erik Kohlros, Gerome Quantmeyer +1
We provide an open source framework to experiment with evolutionary algorithms which we call "Experimenting and Learning toolkit for Evolutionary Algorithms (ELEA)". ELEA is browse…
Analysis of a Gray-Box Operator for Vertex Cover
Samuel Baguley, Tobias Friedrich, Timo Kötzing +3
Combinatorial optimization problems are a prominent application area of evolutionary algorithms, where the (1+1) EA is one of the most investigated. We extend this algorithm by int…
Learning Languages in the Limit from Positive Information with Finitely Many Memory Changes
Timo Kötzing, Karen Seidel
We investigate learning collections of languages from texts by an inductive inference machine with access to the current datum and a bounded memory in form of states. Such a bounde…
Learning Half-Spaces and other Concept Classes in the Limit with Iterative Learners
Ardalan Khazraei, Timo Kötzing, Karen Seidel
In order to model an efficient learning paradigm, iterative learning algorithms access data one by one, updating the current hypothesis without regress to past data. Past research…
Intuitive Analyses via Drift Theory
Andreas Göbel, Timo Kötzing, Martin S. Krejca
Drift theory is an intuitive tool for reasoning about random processes: It allows turning expected stepwise changes into expected first-hitting times. While drift theory is used ex…
First-Hitting Times Under Additive Drift
Timo Kötzing, Martin S. Krejca
For the last ten years, almost every theoretical result concerning the expected run time of a randomized search heuristic used drift theory, making it the arguably most important t…
Learning from Informants: Relations between Learning Success Criteria
Martin Aschenbach, Timo Kötzing, Karen Seidel
Learning from positive and negative information, so-called \emph{informants}, being one of the models for human and machine learning introduced by E.~M.~Gold, is investigated. Part…
A Map of Update Constraints in Inductive Inference
Timo Kötzing, Raphaela Palenta
We investigate how different learning restrictions reduce learning power and how the different restrictions relate to one another. We give a complete map for nine different restric…
Simple Max-Min Ant Systems and the Optimization of Linear Pseudo-Boolean Functions
Timo Kötzing, Frank Neumann, Dirk Sudholt +1
With this paper, we contribute to the understanding of ant colony optimization (ACO) algorithms by formally analyzing their runtime behavior. We study simple MAX-MIN ant systems on…