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%0 Conference Paper
%A Bronni, Jan; Hofmann, Stefanie; Kronfeld, Marcel & Monger, Andreas
%D 2005
%T Eine strategieorientierte, modulare Simulationsumgebung fur mobile Ad-Hoc-Szenarien
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%B Informatiktage 2005
%C Bonn, Germany
%I Gesellschaft f\"ur Informatik (GI)
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%F Kron05GI
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%A Dr\"ager, Andreas; Kronfeld, Marcel; Supper, Jochen; Planatscher, Hannes; Magnus, J\o,rgen B.; Oldiges, Marco & Zell, Andreas
%D 2007
%T Benchmarking Evolutionary Algorithms on Convenience Kinetics Models of the Valine and Leucine Biosynthesis in C.~glutamicum
%E Srinivasan, Dipti & Wang, Lipo
%B IEEE Congress on Evolutionary Computation (CEC 2007)
%C Singapore
%I IEEE Press
%V
%6
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%P 896-903
%&
%Y IEEE Computational Intelligence Society
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%1 http://www.cogsys.cs.uni-tuebingen.de/publikationen/2007/Draeger2007b.pdf
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%F Draeger2007b
%K systems biology, mathematical modeling, benchmark, evolutionary algorithms, valine and leucine biosynthesis, Corynebacterium glutamicum, convenience kinetics
%X An important problem in systems biology is parameter estimation for biochemical system models. Our work concentrates on the metabolic subnetwork of the valine and leucine biosynthesis in \emph{Corynebacterium glutamicum}, an anaerobic actinobacterium of high biotechnological importance. Using data of an in vivo experiment measuring 13 metabolites during a glucose stimulus-response experiment we investigate the performance of various Evolutionary Algorithms on the parameter inference problem in biochemical modeling. Due to the inconclusive information on the reversibility of the reactions in the pathway, we develop both a reversible and an irreversible differential equation model based on the recent convenience kinetics approach. As the reversible model allows better approximation on the whole, we use it to analyze the impact of different settings on four especially promising EAs. We show that Particle Swarm Optimization as well as Differential Evolution are useful methods for parameter estimation on convenience kinetics models outperforming Genetic Algorithm and Evolution Strategy approaches and nearly reaching the quality of independent spline approximations on the raw data.
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%U http://dx.doi.org/10.1109/CEC.2007.4424565
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%0 Journal Article
%A Dr\"ager, Andreas; Kronfeld, Marcel; Ziller, Michael J.; Supper, Jochen; Planatscher, Hannes; Magnus, J\o,rgen B.; Oldiges, Marco; Kohlbacher, Oliver & Zell, Andreas
%D 2009
%T Modeling metabolic networks in C.~glutamicum: a comparison of rate laws in combination with various parameter optimization strategies
%E
%B BMC Systems Biology
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%V 3
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%N 5
%P 5
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%1 http://www.biomedcentral.com/content/pdf/1752-0509-3-5.pdf
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%F Draeger2009a
%K
%X Background: To understand the dynamic behavior of cellular systems, mathematical modeling is often necessary and comprises three steps: (1) experimental measurement of participating molecules, (2) assignment of rate laws to each reaction, and (3) parameter calibration with respect to the measurements. In each of these steps the modeler is confronted with a plethora of alternative approaches, e.g., the selection of approximative rate laws in step two as specific equations are often unknown, or the choice of an estimation procedure with its specific settings in step three. This overall process with its numerous choices and the mutual influence between them makes it hard to single out the best modeling approach for a given problem.
Results: We investigate the modeling process using multiple kinetic equations together with various parameter optimization methods for a well-characterized example network, the biosynthesis of valine and leucine in \emph{C.~glutamicum}. For this purpose, we derive seven dynamic models based on generalized mass action, Michaelis-Menten and convenience kinetics as well as the stochastic Langevin equation. In addition, we introduce two modeling approaches for feedback inhibition to the mass action kinetics. The parameters of each model are estimated using eight optimization strategies. To determine the most promising modeling approaches together with the best optimization algorithms, we carry out a two-step benchmark: (1) coarse-grained comparison of the algorithms on all models and (2) fine-grained tuning of the best optimization algorithms and models. To analyze the space of the best parameters found for each model, we apply clustering, variance, and correlation analysis.
Conclusion: A mixed model based on the convenience rate law and the Michaelis-Menten equation, in which all reactions are assumed to be reversible, is the most suitable deterministic modeling approach followed by a reversible generalized mass action kinetics model. A Langevin model is advisable to take stochastic effects into account. To estimate the model parameters, three algorithms are particularly useful: For first attempts the settings-free Tribes algorithm yields valuable results. Particle swarm optimization and differential evolution provide significantly better results with appropriate settings.
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%U http://www.biomedcentral.com/1752-0509/3/5
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%0 Conference Paper
%A Hofmeister, Marius & Kronfeld, Marcel
%D 2010
%T Multi-robot Coverage Considering Line-of-sight Conditions
%E
%B 7th IFAC Symposium on Intelligent Autonomous Vehicles (IAV)
%C Lecce, Italy
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%U http://www.cogsys.cs.uni-tuebingen.de/publikationen/2010/hofmeister2010iav.pdf
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%0 Conference Paper
%A Hofmeister, Marius; Kronfeld, Marcel & Zell, Andreas
%D 2011
%T Cooperative Visual Mapping in a Heterogeneous Team of Mobile Robots
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%B IEEE International Conference on Robotics and Automation (ICRA)
%C Shanghai, China
%I
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%P 1491-1496
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%U http://www.cogsys.cs.uni-tuebingen.de/publikationen/2011/hofmeister2011icra.pdf
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%0 Computer Program
%A Kronfeld, Marcel
%D 2008
%T EvA2 Short Documentation
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%B
%C Sand 1, 72076 T\"ubingen, Germany
%I Center for Bioinformatics Tuebingen, University of Tuebingen
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%1 http://www.cogsys.cs.uni-tuebingen.de/software/EvA2/EvA2Doc/EvA2Doc.pdf
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%3 manual
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%F EvA2ShortDoc08
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%U http://www.cogsys.cs.uni-tuebingen.de/software/EvA2/shortdoc.html
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%0 Conference Paper
%A Kronfeld, Marcel; Dr\"ager, Andreas; Aschoff, Moritz & Zell, Andreas
%D 2009
%T On the Benefits of Multimodal Optimization for Metabolic Network Modeling
%E Grosse, Ivo; Neumann, Steffen; Posch, Stefan; Schreiber, Falk & Stadler, Peter
%B German Conference on Bioinformatics (GCB 2009)
%C Halle (Saale), Germany
%I German Informatics Society
%V P-157
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%P 191-200
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%S Lecture Notes in Informatics
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%1 http://www.cogsys.cs.uni-tuebingen.de/publikationen/2009/Kron09NichingFinal.pdf
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%F Kron09NichingGCB
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%X The calibration of complex models of biological systems requires numerical simulation and optimization procedures to infer undetermined parameters and fit measured data. The optimization step typically employs heuristic global optimization algorithms, but due to measurement noise and the many degrees of freedom, it is not guaranteed that the identified single optimum is also the most meaningful parameter set. Multimodal optimization allows for identifying multiple optima in parallel. We consider high-dimensional benchmark functions and a realistic metabolic network model from systems biology to compare evolutionary and swarm-based multimodal methods. We show that an extended swarm based niching algorithm is able to find a considerable set of solutions in parallel, which have significantly more explanatory power. As an outline of the information gain, the variations in the set of high-quality solutions are contrasted to a state-of-the-art global sensitivity analysis.
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%U http://www.gcb2009.de/program.php
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%0 Conference Paper
%A Kronfeld, Marcel; Planatscher, Hannes & Zell, Andreas
%D 2010
%T The EvA2 Optimization Framework
%E Blum, Christian & Battiti, Roberto
%B Learning and Intelligent Optimization Conference, Special Session on Software for Optimization (LION-SWOP)
%C Venice, Italy
%I Springer Verlag
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%P 247-250
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%S Lecture Notes in Computer Science, LNCS
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%F Kron10EvA2
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%X We present EvA2, a comprehensive metaheuristic optimization framework with emphasis on Evolutionary Algorithms implemented in Java. It presents a modular structure of interfaces and abstract classes for the implementation of optimization problems. End users may choose among several layers of abstraction for an entrance point meeting their requirements on both ease of use and access to extensive functionality. The EvA2 framework has been applied successfully in several academic as well as industrial cooperations and is extended continuously. It is freely available under an open source license (LGPL).
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%U http://www.cogsys.cs.uni-tuebingen.de/publikationen/2010/Kron10EvA2Short.pdf
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%0 Journal Article
%A Kronfeld, Marcel; Weiss, Christian & Zell, Andreas
%D 2010
%T Swarm-supported Outdoor Localization with Sparse Visual Data
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%B Robotics and Autonomous Systems. Selected papers from the 2007 European Conference on Mobile Robots (ECMR 2007)
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%V 58
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%P 166-173
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%1 http://www.cogsys.cs.uni-tuebingen.de/publikationen/2009/Kron09SwarmSupp.pdf
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%F Kron10LocRAS
%K Outdoor robotics; Robot vision; Visual localization; Swarm intelligence; Particle swarm optimization
%X The localization of mobile systems with video data is a challenging field in robotic vision research. Apart from support technologies like GPS, a self-sufficient visual system is desirable. We introduce a new heuristic approach to outdoor localization in a scenario with sparse visual data and without odometry readings. Localization is interpreted as an optimization problem, and a swarm-based optimization method is adapted and applied, remaining independent of the specific visual feature type. The new method obtains similar or better localization results in our experiments while requiring only two-thirds of the number of image comparisons, indicating an all-over speed-up by 25%.
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%U http://www.sciencedirect.com/science/article/B6V16-4X908T5-3/2/559434e23a220afad0b33d51ed559198
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%0 Conference Paper
%A Kronfeld, Marcel; Weiss, Christian & Zell, Andreas
%D 2008
%T A Dynamic Swarm for Visual Location Tracking
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%B 6th International Conference on Ant Colony Optimization and Swarm Intelligence ({ANTS} 2008)
%C Brussels, Belgium
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%P 203-210
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%U http://www.cogsys.cs.uni-tuebingen.de/publikationen/2008/Kron08DynLoc.pdf
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%A Kronfeld, Marcel; Weiss, Christian & Zell, Andreas
%D 2007
%T Swarm-supported Outdoor Localization with Sparse Visual Data
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%B 3rd European Conference on Mobile Robots (ECMR 2007)
%C Freiburg, Germany
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%P 259-264
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%F Kron07Loc
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%X The localization of mobile systems with video data is a challenging field in robotic vision research. Apart from artificial environmental support technologies like GPS localization, a selfsufficient visual system is desirable. In this work, we introduce a new heuristic approach to outdoor localization in a scenario where no odometry readings are available. In an earlier work, we employed SIFT features and a common particle filter method in the scenario. A modification of Particle Swarm Optimization, a popular optimization technique especially in dynamically changing environments, is developed and fit to the localization problem, including self-adaptive mechanisms. The new method obtains similar or better localization results in our experiments, while requiring a fraction of SIFT comparisons of the standard method, indicating an all-over speed-up by 25%.
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%U http://www.cogsys.cs.uni-tuebingen.de/publikationen/2007/Kron07SwarmSuppFinal.pdf
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%A Kronfeld, Marcel & Zell, Andreas
%D 2010
%T Gaussian Process assisted Particle Swarm Optimization
%E Blum, Christian & Battiti, Roberto
%B Learning and Intelligent Optimization Conference (LION IV)
%C Venice, Italy
%I Springer Verlag
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%P 139-153
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%S Lecture Notes in Computer Science, LNCS
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%F Kron10GPPSO
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%X Real-world optimization problems often are non-convex, non-differentiable and highly multimodal, which is why stochastic, population-based metaheuristics are frequently applied. If the optimization problem is also computationally very expensive, only relatively few function evaluations can be afforded. We develop a model-assisted optimization approach as a coupling of Gaussian Process modeling, a regression technique from machine learning, with the Particle Swarm Optimization metaheuristic. It uses earlier function evaluations to predict areas of improvement and exploits the model information in the heuristic search. Under the assumption of a costly target function, it is shown that model-assistance improves the performance across a set of standard benchmark functions. In return, it is possible to reduce the number of target function evaluations to reach a certain fitness level to speed up the search.
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%A Kronfeld, Marcel & Zell, Andreas
%D 2010
%T Towards Scalability in Niching Methods
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%B Proceedings of the IEEE Congress on Evolutionary Computation (CEC)
%C Barcelona, Spain
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%P 4409-4416
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%F Kron10Niching
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%X The scaling properties of multimodal optimization methods have seldom been studied, and existing studies often concentrated on the idea that all local optima of a multimodal function can be found and their number can be estimated a priori. We argue that this approach is impractical for complex, high-dimensional target functions, and we formulate alternative criteria for scalable multimodal optimization methods. We suggest that a scalable niching method should return the more local optima the longer it is run, without relying on a fixed number of expected optima. This can be fulfilled by sequential and semi-sequential niching methods, several of which are presented and analyzed in that respect. Results show that, while sequential local search is very successful on simpler functions, a clustering-based particle swarm approach is most successful on multi-funnel functions, offering scalability even under deceptive multimodality, and denoting it a starting point towards effective scalable niching.
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%U http://www.cogsys.cs.uni-tuebingen.de/publikationen/2010/Kron10Scaling.pdf
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%0 Journal Article
%A Wrzodek, Clemens; Schr\"oder, Adrian; Dr\"ager, Andreas; Wanke, Dierk; Berendzen, Kenneth W.; Kronfeld, Marcel; Harter, Klaus & Zell, Andreas
%D 2010
%T ModuleMaster: A new tool to decipher transcriptional regulatory networks
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%B Biosystems
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%I Elsevier
%V 99
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%N 1
%P 79-81
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%1 http://www.cogsys.cs.uni-tuebingen.de/publikationen/2010/wrzodek2010_ModuleMaster.pdf
%2 http://www.cogsys.cs.uni-tuebingen.de/software/ModuleMaster/
%3 article
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%F Wrzodek2010
%K Gene regulation, Cis-regulatory modules, Regulatory sequence analysis, Matrix scan, Transcription factors
%X In this article we present ModuleMaster, a novel application for finding \emph{cis}-regulatory modules (CRMs) in sets of co-expressed genes. The application comes with a newly developed method which not only considers transcription factor binding information but also multivariate functional relationships between regulators and target genes to improve the detection of CRMs. Given only the results of a microarray and a subsequent clustering experiment, the program includes all necessary data and algorithms to perform every step to find CRMs. This workbench possesses an easy-to-use graphical user interface, together with job-processing and command-line options, making ModuleMaster a sophisticated program for large-scale batch processing. The detected CRMs can be visualized and evaluated in various ways, i.e., generating GraphML- and R-based whole regulatory network visualizations or generating SBML files for subsequent analytical processing and dynamic modeling. Availability: ModuleMaster is freely available to academics as a webstart application and for download at \url{http://www.ra.cs.uni-tuebingen.de/software/ModuleMaster/}, including comprehensive documentation.
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%U http://dx.doi.org/10.1016/j.biosystems.2009.09.005
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