@inproceedings{1022, author = {{Woldegebreal, Dereje H and Karl, Holger}}, booktitle = {{Sensor Technologies and Applications (SENSORCOMM), 2010 Fourth International Conference on}}, pages = {{513----520}}, title = {{{Energy efficiency model of network-coded cooperation in wireless sensor networks}}}, year = {{2010}}, } @inproceedings{1023, author = {{Khan, Rana Azeem M and Karl, Holger}}, booktitle = {{Proceedings of the Fourth International Conference on Mobile Ubiquitous Computing (UBICOMM 2010)}}, title = {{{Cooperative communication to improve reliability and efficient neighborhood wakeup in wireless sensor networks}}}, year = {{2010}}, } @inproceedings{13589, author = {{Dembczynski, K. and Cheng, W. and Hüllermeier, Eyke}}, booktitle = {{in Proceedings ICML-2010, 27th International Conference on Machine Learning, Haifa, Israel}}, editor = {{Fürnkranz, J. and Joachims, T.}}, pages = {{279--286}}, title = {{{Bayes optimal multilabel classification via probalistic classifier chains}}}, year = {{2010}}, } @inproceedings{13590, author = {{Cheng, W. and Dembczynski, K. and Hüllermeier, Eyke}}, booktitle = {{in Proceedings ICML-2010, 27th International Conference on Machine Learning, Haifa, Israel}}, editor = {{Fürnkranz, J. and Joachims, T.}}, pages = {{215--222}}, title = {{{Label ranking based on the Plackett-Luce model}}}, year = {{2010}}, } @inproceedings{13591, author = {{Cheng, W. and Dembczynski, K. and Hüllermeier, Eyke}}, booktitle = {{in Proceedings ICML-2010, 27th International Conference on Machine Learning, Haifa, Israel}}, editor = {{Fürnkranz, J. and Joachims, T.}}, title = {{{Graded multi-label classification: The ordinal case}}}, year = {{2010}}, } @inproceedings{13593, author = {{Dembczynski, K. and Waegeman, W. and Cheng, W. and Hüllermeier, Eyke}}, booktitle = {{In Proceedings ECML/PKDD-2010, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ,Bareclona, Spain}}, title = {{{Regret analysis for performance metrics in multi-label classification: The case of Hamming and subset zero-one loss}}}, year = {{2010}}, } @inproceedings{13594, author = {{Cheng, W. and Rademaker, M. and De Beats, B. and Hüllermeier, Eyke}}, booktitle = {{In Proceedings ECML/PKDD-2010, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ,Bareclona, Spain}}, title = {{{Predicting partial orders: Ranking with abstention}}}, year = {{2010}}, } @inproceedings{13597, author = {{Hüllermeier, Eyke}}, booktitle = {{in Proceedings SUM 2010, International Conference on Scalable Uncertainty Management }}, number = {{LNCS}}, pages = {{16--19}}, publisher = {{Springer}}, title = {{{Uncertainty in clustering and classification }}}, volume = {{6379}}, year = {{2010}}, } @inproceedings{13640, author = {{Giefers, Heiner and Platzner, Marco}}, booktitle = {{Proceedings of the 20th International Conference on Field Programmable Logic and Applications (FPL)}}, publisher = {{IEEE}}, title = {{{A Triple Hybrid Interconnect for Many-Cores: Reconfigurable Mesh, NoC and Barrier}}}, year = {{2010}}, } @inproceedings{13641, author = {{Schäfer, Wilhelm and Birattari, Mauro and Blömer, Johannes and Dorigo, Marco and Engels, Gregor and O'Grady, Rehan and Platzner, Marco and Rammig, Franz-Josef and Reif, Wolfgang and Trächtler, Ansgar}}, booktitle = {{Proceedings of the Foundations of Software Engineering (FSE) and NITR & D/SPD Working Conference on the Future of Software Engineering Research (FoSER)}}, pages = {{321--324}}, title = {{{Engineering Self-Coordinating Software Intensive Systems}}}, year = {{2010}}, } @inproceedings{13642, author = {{Giefers, Heiner and Platzner, Marco}}, booktitle = {{Proceedings of the 10th International Conference on Engineering of Reconfigurable Systems and Algorithms (ERSA)}}, publisher = {{CSREA Press}}, title = {{{A Self-Reconfigurable Lightweight Interconnect for Scalable Processor Fabrics}}}, year = {{2010}}, } @misc{47684, author = {{Postler, Tim}}, keywords = {{Diplomarbeit}}, title = {{{Smart Card basierte Berechnung einer Gruppensignatur als Teil einer biometrischen Authentisierung}}}, year = {{2010}}, } @inproceedings{46408, abstract = {{The integration of experts’ preferences is an important aspect in multi-objective optimization. Usually, one out of a set of Pareto optimal solutions has to be chosen based on expert knowledge. A combination of multi-objective particle swarm optimization (MOPSO) with the desirability concept is introduced to efficiently focus on desired and relevant regions of the true Pareto front of the optimization problem which facilitates the solution selection process. Desirability functions of the objectives are optimized, and the desirability index is used for selecting the global best particle in each iteration. The resulting MOPSO variant DF-MOPSO in most cases exclusively generates solutions in the desired area of the Pareto front. Approximations of the whole Pareto front result in cases of misspecified desired regions.}}, author = {{Mostaghim, Sanaz and Trautmann, Heike and Mersmann, Olaf}}, booktitle = {{Parallel Problem Solving from Nature, PPSN XI}}, editor = {{Schaefer, Robert and Cotta, Carlos and Kołodziej, Joanna and Rudolph, Günter}}, isbn = {{978-3-642-15871-1}}, pages = {{101–110}}, publisher = {{Springer Berlin Heidelberg}}, title = {{{Preference-Based Multi-Objective Particle Swarm Optimization Using Desirabilities}}}, doi = {{https://doi.org/10.1007/978-3-642-15871-1_11}}, year = {{2010}}, } @inproceedings{46405, abstract = {{We present methods to answer two basic questions that arise when benchmarking optimization algorithms. The first one is: which algorithm is the ’best’ one? and the second one: which algorithm should I use for my real world problem? Both are connected and neither is easy to answer. We present methods which can be used to analyse the raw data of a benchmark experiment and derive some insight regarding the answers to these questions. We employ the presented methods to analyse the BBOB’09 benchmark results and present some initial findings.}}, author = {{Mersmann, Olaf and Preuss, Mike and Trautmann, Heike}}, booktitle = {{Proceedings of the 11th International Conference on Parallel Problem Solving from Nature: Part I}}, isbn = {{3642158439}}, keywords = {{benchmarking, multidimensional scaling, consensus ranking, evolutionary optimization, BBOB test set}}, pages = {{73–82}}, publisher = {{Springer-Verlag}}, title = {{{Benchmarking Evolutionary Algorithms: Towards Exploratory Landscape Analysis}}}, year = {{2010}}, } @inproceedings{46406, abstract = {{We present methods to answer two basic questions that arise when benchmarking optimization algorithms. The first one is: which algorithm is the 'best' one? and the second one: which algorithm should I use for my real world problem? Both are connected and neither is easy to answer. We present methods which can be used to analyse the raw data of a benchmark experiment and derive some insight regarding the answers to these questions. We employ the presented methods to analyse the BBOB'09 benchmark results and present some initial findings.}}, author = {{Mersmann, O and Trautmann, Heike and Naujoks, B and Weihs, C}}, booktitle = {{Learning and Intelligent Optimization, 4$^th$ International Conference, LION 4, Venice, Italy}}, editor = {{Blum, C and Battiti, R}}, pages = {{333–337}}, publisher = {{Springer}}, title = {{{On the Distribution of EMOA Hypervolumes}}}, volume = {{6073}}, year = {{2010}}, } @inproceedings{46407, abstract = {{Choosing and tuning an optimization procedure for a given class of nonlinear optimization problems is not an easy task. One way to proceed is to consider this as a tournament, where each procedure will compete in different ‘disciplines’. Here, disciplines could either be different functions, which we want to optimize, or specific performance measures of the optimization procedure. We would then be interested in the algorithm that performs best in a majority of cases or whose average performance is maximal. We will focus on evolutionary multiobjective optimization algorithms (EMOA), and will present a novel approach to the design and analysis of evolutionary multiobjective benchmark experiments based on similar work from the context of machine learning. We focus on deriving a consensus among several benchmarks over different test problems and illustrate the methodology by reanalyzing the results of the CEC 2007 EMOA competition.}}, author = {{Mersmann, Olaf and Trautmann, Heike and Naujoks, Boris and Weihs, Claus}}, booktitle = {{IEEE Congress on Evolutionary Computation}}, issn = {{1941-0026}}, pages = {{1--8}}, title = {{{Benchmarking evolutionary multiobjective optimization algorithms}}}, doi = {{10.1109/CEC.2010.5586241}}, year = {{2010}}, } @inproceedings{46404, author = {{Ding, J and Wessing, S and Trautmann, Heike and Mehnen, J and Naujoks, B}}, booktitle = {{Proceedings of the 7$^th$ CIRP International Seminar on Intelligent Computation in Manufacturing Engineering (CIRP ICME ’10)}}, editor = {{Teti, R}}, publisher = {{Copyright C.O.C. Com. org. Conv.}}, title = {{{Sequential Parameter Optimisation for Multi-Objective Evolutionary Optimisation of Additive Layer Manufacturing}}}, year = {{2010}}, } @inproceedings{46409, abstract = {{Since many real-world optimization problems are noisy, vector optimization algorithms that can cope with noise and uncertainty are required. We propose new, robust selection strategies for evolutionary multi-objective optimization in the presence of noise. We apply new measures of uncertainty for estimating the recently introduced Pareto-dominance for uncertain and noisy environments (PDU). The first measure is the inter-quartile range of the outcomes of repeated function evaluations. The second is based on axis-aligned bounding boxes around the upper and lower quantiles of the sampled fitness values in objective space. Experiments on real and artificial problems show promising results.}}, author = {{Voß, Thomas and Trautmann, Heike and Igel, Christian}}, booktitle = {{Parallel Problem Solving from Nature, PPSN XI}}, editor = {{Schaefer, Robert and Cotta, Carlos and Kołodziej, Joanna and Rudolph, Günter}}, isbn = {{978-3-642-15871-1}}, pages = {{260–269}}, publisher = {{Springer Berlin Heidelberg}}, title = {{{New Uncertainty Handling Strategies in Multi-objective Evolutionary Optimization}}}, doi = {{https://doi.org/10.1007/978-3-642-15871-1_27}}, year = {{2010}}, } @article{46412, abstract = {{In this paper, a concept for efficiently approximating the practically relevant regions of the Pareto front (PF) is introduced. Instead of the original objectives, desirability functions (DFs) of the objectives are optimized, which express the preferences of the decision maker. The original problem formulation and the optimization algorithm do not have to be modified. DFs map an objective to the domain [0, 1] and nonlinearly increase with better objective quality. By means of this mapping, values of different objectives and units become comparable. A biased distribution of the solutions in the PF approximation based on different scalings of the objectives is prevented. Thus, we propose the integration of DFs into the S-metric selection evolutionary multiobjective algorithm. The transformation ensures the meaning of the hypervolumes internally computed. Furthermore, it is shown that the reference point for the hypervolume calculation can be set intuitively. The approach is analyzed using standard test problems. Moreover, a practical validation by means of the optimization of a turning process is performed.}}, author = {{Wagner, Tobias and Trautmann, Heike}}, issn = {{1941-0026}}, journal = {{IEEE Transactions on Evolutionary Computation}}, number = {{5}}, pages = {{688--701}}, title = {{{Integration of Preferences in Hypervolume-Based Multiobjective Evolutionary Algorithms by Means of Desirability Functions}}}, doi = {{10.1109/TEVC.2010.2058119}}, volume = {{14}}, year = {{2010}}, } @article{46411, abstract = {{The paper presents a framework to optimise the design of work roll based on the cooling performance. The framework develops meta-models from a set of finite element analyses (FEA) of the roll cooling. A design of experiment technique is used to identify the FEA runs. The research also identifies sources of uncertainties in the design process. A robust evolutionary multi-objective evaluation technique is applied to the design optimisation in constrained problems with real life uncertainty. The approach handles uncertainties associated both with design variables and fitness functions. Constraints violation within the neighbourhood of a design is considered as part of a measurement for degree of feasibility and robustness of a solution.}}, author = {{Azene, Y.T. and Roy, R. and Farrugia, D. and Onisa, C. and Mehnen, J. and Trautmann, Heike}}, issn = {{1755-5817}}, journal = {{CIRP Journal of Manufacturing Science and Technology}}, keywords = {{Roll cooling design, Uncertainty, Design optimisation, Multi-objective optimisation, Constraint in design}}, number = {{4}}, pages = {{290--298}}, title = {{{Work roll cooling system design optimisation in presence of uncertainty and constrains}}}, doi = {{https://doi.org/10.1016/j.cirpj.2010.06.001}}, volume = {{2}}, year = {{2010}}, }