@book{AdrianoBleifussChengetal.2019, author = {Adriano, Christian and Bleifuß, Tobias and Cheng, Lung-Pan and Diba, Kiarash and Fricke, Andreas and Grapentin, Andreas and Jiang, Lan and Kovacs, Robert and Krejca, Martin Stefan and Mandal, Sankalita and Marwecki, Sebastian and Matthies, Christoph and Mattis, Toni and Niephaus, Fabio and Pirl, Lukas and Quinzan, Francesco and Ramson, Stefan and Rezaei, Mina and Risch, Julian and Rothenberger, Ralf and Roumen, Thijs and Stojanovic, Vladeta and Wolf, Johannes}, title = {Technical report}, number = {129}, editor = {Meinel, Christoph and Plattner, Hasso and D{\"o}llner, J{\"u}rgen Roland Friedrich and Weske, Mathias and Polze, Andreas and Hirschfeld, Robert and Naumann, Felix and Giese, Holger and Baudisch, Patrick and Friedrich, Tobias and B{\"o}ttinger, Erwin and Lippert, Christoph}, publisher = {Universit{\"a}tsverlag Potsdam}, address = {Potsdam}, isbn = {978-3-86956-465-4}, issn = {1613-5652}, doi = {10.25932/publishup-42753}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-427535}, publisher = {Universit{\"a}t Potsdam}, pages = {vi, 267}, year = {2019}, abstract = {Design and Implementation of service-oriented architectures imposes a huge number of research questions from the fields of software engineering, system analysis and modeling, adaptability, and application integration. Component orientation and web services are two approaches for design and realization of complex web-based system. Both approaches allow for dynamic application adaptation as well as integration of enterprise application. Commonly used technologies, such as J2EE and .NET, form de facto standards for the realization of complex distributed systems. Evolution of component systems has lead to web services and service-based architectures. This has been manifested in a multitude of industry standards and initiatives such as XML, WSDL UDDI, SOAP, etc. All these achievements lead to a new and promising paradigm in IT systems engineering which proposes to design complex software solutions as collaboration of contractually defined software services. Service-Oriented Systems Engineering represents a symbiosis of best practices in object-orientation, component-based development, distributed computing, and business process management. It provides integration of business and IT concerns. The annual Ph.D. Retreat of the Research School provides each member the opportunity to present his/her current state of their research and to give an outline of a prospective Ph.D. thesis. Due to the interdisciplinary structure of the research school, this technical report covers a wide range of topics. These include but are not limited to: Human Computer Interaction and Computer Vision as Service; Service-oriented Geovisualization Systems; Algorithm Engineering for Service-oriented Systems; Modeling and Verification of Self-adaptive Service-oriented Systems; Tools and Methods for Software Engineering in Service-oriented Systems; Security Engineering of Service-based IT Systems; Service-oriented Information Systems; Evolutionary Transition of Enterprise Applications to Service Orientation; Operating System Abstractions for Service-oriented Computing; and Services Specification, Composition, and Enactment.}, language = {en} } @misc{BlaesiusEubeFeldtkelleretal.2018, author = {Blaesius, Thomas and Eube, Jan and Feldtkeller, Thomas and Friedrich, Tobias and Krejca, Martin Stefan and Lagodzinski, Gregor J. A. and Rothenberger, Ralf and Severin, Julius and Sommer, Fabian and Trautmann, Justin}, title = {Memory-restricted Routing With Tiled Map Data}, series = {2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)}, journal = {2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)}, publisher = {IEEE}, address = {New York}, isbn = {978-1-5386-6650-0}, issn = {1062-922X}, doi = {10.1109/SMC.2018.00567}, pages = {3347 -- 3354}, year = {2018}, abstract = {Modern routing algorithms reduce query time by depending heavily on preprocessed data. The recently developed Navigation Data Standard (NDS) enforces a separation between algorithms and map data, rendering preprocessing inapplicable. Furthermore, map data is partitioned into tiles with respect to their geographic coordinates. With the limited memory found in portable devices, the number of tiles loaded becomes the major factor for run time. We study routing under these restrictions and present new algorithms as well as empirical evaluations. Our results show that, on average, the most efficient algorithm presented uses more than 20 times fewer tile loads than a normal A*.}, language = {en} } @article{DoerrKrejca2021, author = {Doerr, Benjamin and Krejca, Martin Stefan}, title = {A simplified run time analysis of the univariate marginal distribution algorithm on LeadingOnes}, series = {Theoretical computer science}, volume = {851}, journal = {Theoretical computer science}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0304-3975}, doi = {10.1016/j.tcs.2020.11.028}, pages = {121 -- 128}, year = {2021}, abstract = {With elementary means, we prove a stronger run time guarantee for the univariate marginal distribution algorithm (UMDA) optimizing the LEADINGONES benchmark function in the desirable regime with low genetic drift. If the population size is at least quasilinear, then, with high probability, the UMDA samples the optimum in a number of iterations that is linear in the problem size divided by the logarithm of the UMDA's selection rate. This improves over the previous guarantee, obtained by Dang and Lehre (2015) via the deep level-based population method, both in terms of the run time and by demonstrating further run time gains from small selection rates. Under similar assumptions, we prove a lower bound that matches our upper bound up to constant factors.}, language = {en} } @article{FriedrichKrejcaRothenbergeretal.2019, author = {Friedrich, Tobias and Krejca, Martin Stefan and Rothenberger, Ralf and Arndt, Tobias and Hafner, Danijar and Kellermeier, Thomas and Krogmann, Simon and Razmjou, Armin}, title = {Routing for on-street parking search using probabilistic data}, series = {AI communications : AICOM ; the European journal on artificial intelligence}, volume = {32}, journal = {AI communications : AICOM ; the European journal on artificial intelligence}, number = {2}, publisher = {IOS Press}, address = {Amsterdam}, issn = {0921-7126}, doi = {10.3233/AIC-180574}, pages = {113 -- 124}, year = {2019}, abstract = {A significant percentage of urban traffic is caused by the search for parking spots. One possible approach to improve this situation is to guide drivers along routes which are likely to have free parking spots. The task of finding such a route can be modeled as a probabilistic graph problem which is NP-complete. Thus, we propose heuristic approaches for solving this problem and evaluate them experimentally. For this, we use probabilities of finding a parking spot, which are based on publicly available empirical data from TomTom International B.V. Additionally, we propose a heuristic that relies exclusively on conventional road attributes. Our experiments show that this algorithm comes close to the baseline by a factor of 1.3 in our cost measure. Last, we complement our experiments with results from a field study, comparing the success rates of our algorithms against real human drivers.}, language = {en} } @article{FriedrichKoetzingKrejca2019, author = {Friedrich, Tobias and K{\"o}tzing, Timo and Krejca, Martin Stefan}, title = {Unbiasedness of estimation-of-distribution algorithms}, series = {Theoretical computer science}, volume = {785}, journal = {Theoretical computer science}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0304-3975}, doi = {10.1016/j.tcs.2018.11.001}, pages = {46 -- 59}, year = {2019}, abstract = {In the context of black-box optimization, black-box complexity is used for understanding the inherent difficulty of a given optimization problem. Central to our understanding of nature-inspired search heuristics in this context is the notion of unbiasedness. Specialized black-box complexities have been developed in order to better understand the limitations of these heuristics - especially of (population-based) evolutionary algorithms (EAs). In contrast to this, we focus on a model for algorithms explicitly maintaining a probability distribution over the search space: so-called estimation-of-distribution algorithms (EDAs). We consider the recently introduced n-Bernoulli-lambda-EDA framework, which subsumes, for example, the commonly known EDAs PBIL, UMDA, lambda-MMAS(IB), and cGA. We show that an n-Bernoulli-lambda-EDA is unbiased if and only if its probability distribution satisfies a certain invariance property under isometric automorphisms of [0, 1](n). By restricting how an n-Bernoulli-lambda-EDA can perform an update, in a way common to many examples, we derive conciser characterizations, which are easy to verify. We demonstrate this by showing that our examples above are all unbiased. (C) 2018 Elsevier B.V. All rights reserved.}, language = {en} } @article{FriedrichKoetzingKrejcaetal.2016, author = {Friedrich, Tobias and K{\"o}tzing, Timo and Krejca, Martin Stefan and Sutton, Andrew M.}, title = {Robustness of Ant Colony Optimization to Noise}, series = {Evolutionary computation}, volume = {24}, journal = {Evolutionary computation}, publisher = {MIT Press}, address = {Cambridge}, issn = {1063-6560}, doi = {10.1162/EVCO_a_00178}, pages = {237 -- 254}, year = {2016}, abstract = {Recently, ant colony optimization (ACO) algorithms have proven to be efficient in uncertain environments, such as noisy or dynamically changing fitness functions. Most of these analyses have focused on combinatorial problems such as path finding. We rigorously analyze an ACO algorithm optimizing linear pseudo- Boolean functions under additive posterior noise. We study noise distributions whose tails decay exponentially fast, including the classical case of additive Gaussian noise. Without noise, the classical (mu + 1) EA outperforms any ACO algorithm, with smaller mu being better; however, in the case of large noise, the (mu + 1) EA fails, even for high values of mu (which are known to help against small noise). In this article, we show that ACO is able to deal with arbitrarily large noise in a graceful manner; that is, as long as the evaporation factor. is small enough, dependent on the variance s2 of the noise and the dimension n of the search space, optimization will be successful. We also briefly consider the case of prior noise and prove that ACO can also efficiently optimize linear functions under this noise model.}, language = {en} } @phdthesis{Krejca2019, author = {Krejca, Martin Stefan}, title = {Theoretical analyses of univariate estimation-of-distribution algorithms}, doi = {10.25932/publishup-43487}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-434870}, school = {Universit{\"a}t Potsdam}, pages = {xii, 243}, year = {2019}, abstract = {Optimization is a core part of technological advancement and is usually heavily aided by computers. However, since many optimization problems are hard, it is unrealistic to expect an optimal solution within reasonable time. Hence, heuristics are employed, that is, computer programs that try to produce solutions of high quality quickly. One special class are estimation-of-distribution algorithms (EDAs), which are characterized by maintaining a probabilistic model over the problem domain, which they evolve over time. In an iterative fashion, an EDA uses its model in order to generate a set of solutions, which it then uses to refine the model such that the probability of producing good solutions is increased. In this thesis, we theoretically analyze the class of univariate EDAs over the Boolean domain, that is, over the space of all length-n bit strings. In this setting, the probabilistic model of a univariate EDA consists of an n-dimensional probability vector where each component denotes the probability to sample a 1 for that position in order to generate a bit string. My contribution follows two main directions: first, we analyze general inherent properties of univariate EDAs. Second, we determine the expected run times of specific EDAs on benchmark functions from theory. In the first part, we characterize when EDAs are unbiased with respect to the problem encoding. We then consider a setting where all solutions look equally good to an EDA, and we show that the probabilistic model of an EDA quickly evolves into an incorrect model if it is always updated such that it does not change in expectation. In the second part, we first show that the algorithms cGA and MMAS-fp are able to efficiently optimize a noisy version of the classical benchmark function OneMax. We perturb the function by adding Gaussian noise with a variance of σ², and we prove that the algorithms are able to generate the true optimum in a time polynomial in σ² and the problem size n. For the MMAS-fp, we generalize this result to linear functions. Further, we prove a run time of Ω(n log(n)) for the algorithm UMDA on (unnoisy) OneMax. Last, we introduce a new algorithm that is able to optimize the benchmark functions OneMax and LeadingOnes both in O(n log(n)), which is a novelty for heuristics in the domain we consider.}, language = {en} } @article{KoetzingKrejca2019, author = {K{\"o}tzing, Timo and Krejca, Martin Stefan}, title = {First-hitting times under drift}, series = {Theoretical computer science}, volume = {796}, journal = {Theoretical computer science}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0304-3975}, doi = {10.1016/j.tcs.2019.08.021}, pages = {51 -- 69}, year = {2019}, abstract = {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 tool in this domain. Its success is due to its ease of use and its powerful result: drift theory allows the user to derive bounds on the expected first-hitting time of a random process by bounding expected local changes of the process - the drift. This is usually far easier than bounding the expected first-hitting time directly. Due to the widespread use of drift theory, it is of utmost importance to have the best drift theorems possible. We improve the fundamental additive, multiplicative, and variable drift theorems by stating them in a form as general as possible and providing examples of why the restrictions we keep are still necessary. Our additive drift theorem for upper bounds only requires the process to be lower-bounded, that is, we remove unnecessary restrictions like a finite, discrete, or bounded state space. As corollaries, the same is true for our upper bounds in the case of variable and multiplicative drift. By bounding the step size of the process, we derive new lower-bounding multiplicative and variable drift theorems. Last, we also state theorems that are applicable when the process has a drift of 0, by using a drift on the variance of the process.}, language = {en} } @misc{KoetzingKrejca2018, author = {K{\"o}tzing, Timo and Krejca, Martin Stefan}, title = {First-Hitting times under additive drift}, series = {Parallel Problem Solving from Nature - PPSN XV, PT II}, volume = {11102}, journal = {Parallel Problem Solving from Nature - PPSN XV, PT II}, publisher = {Springer}, address = {Cham}, isbn = {978-3-319-99259-4}, issn = {0302-9743}, doi = {10.1007/978-3-319-99259-4_8}, pages = {92 -- 104}, year = {2018}, abstract = {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 tool in this domain. Its success is due to its ease of use and its powerful result: drift theory allows the user to derive bounds on the expected first-hitting time of a random process by bounding expected local changes of the process - the drift. This is usually far easier than bounding the expected first-hitting time directly. Due to the widespread use of drift theory, it is of utmost importance to have the best drift theorems possible. We improve the fundamental additive, multiplicative, and variable drift theorems by stating them in a form as general as possible and providing examples of why the restrictions we keep are still necessary. Our additive drift theorem for upper bounds only requires the process to be nonnegative, that is, we remove unnecessary restrictions like a finite, discrete, or bounded search space. As corollaries, the same is true for our upper bounds in the case of variable and multiplicative drift.}, language = {en} } @misc{KoetzingKrejca2018, author = {K{\"o}tzing, Timo and Krejca, Martin Stefan}, title = {First-Hitting times for finite state spaces}, series = {Parallel Problem Solving from Nature - PPSN XV, PT II}, volume = {11102}, journal = {Parallel Problem Solving from Nature - PPSN XV, PT II}, publisher = {Springer}, address = {Cham}, isbn = {978-3-319-99259-4}, issn = {0302-9743}, doi = {10.1007/978-3-319-99259-4_7}, pages = {79 -- 91}, year = {2018}, abstract = {One of the most important aspects of a randomized algorithm is bounding its expected run time on various problems. Formally speaking, this means bounding the expected first-hitting time of a random process. The two arguably most popular tools to do so are the fitness level method and drift theory. The fitness level method considers arbitrary transition probabilities but only allows the process to move toward the goal. On the other hand, drift theory allows the process to move into any direction as long as it move closer to the goal in expectation; however, this tendency has to be monotone and, thus, the transition probabilities cannot be arbitrary. We provide a result that combines the benefit of these two approaches: our result gives a lower and an upper bound for the expected first-hitting time of a random process over {0,..., n} that is allowed to move forward and backward by 1 and can use arbitrary transition probabilities. In case that the transition probabilities are known, our bounds coincide and yield the exact value of the expected first-hitting time. Further, we also state the stationary distribution as well as the mixing time of a special case of our scenario.}, language = {en} }