@inbook{16407, abstract = {{Many virtual 3D scenes, especially those that are large, are not structured evenly. For such heterogeneous data, there is no single algorithm that is able to render every scene type at each position fast and with the same high image quality. For a small set of scenes, this situation can be improved if different rendering algorithms are manually assigned to particular parts of the scene by an experienced user. We introduce the Multi-Algorithm-Rendering method. It automatically deploys different rendering algorithms simultaneously for a broad range of scene types. The method divides the scene into subregions and measures the behavior of different algorithms for each region in a preprocessing step. During runtime, this data is utilized to compute an estimate for the quality and running time of the available rendering algorithms from the observer's point of view. By solving an optimizing problem, the image quality can be optimized by an assignment of algorithms to regions while keeping the frame rate almost constant. }}, author = {{Petring, Ralf and Eikel, Benjamin and Jähn, Claudius and Fischer, Matthias and Meyer auf der Heide, Friedhelm}}, booktitle = {{Advances in Visual Computing}}, isbn = {{9783642419133}}, issn = {{0302-9743}}, title = {{{Real-Time 3D Rendering of Heterogeneous Scenes}}}, doi = {{10.1007/978-3-642-41914-0_44}}, year = {{2013}}, } @inproceedings{13115, author = {{Szarvas, G. and Busa-Fekete, Robert and Hüllermeier, Eyke}}, booktitle = {{In Proceedings EMNLP-2013 Conference on Empirical Methods in Natural Language Processing, Seattle, USA}}, title = {{{Learning to rank lexical substitutions}}}, year = {{2013}}, } @inproceedings{13116, author = {{Dembczynski, K. and Jachnik, A. and Kotlowski, W. and Waegeman, W. and Hüllermeier, Eyke}}, booktitle = {{in Proceedings ICML-2013, 30th International Conference on Machine Learning, Atlanta, USA}}, editor = {{Dasgupta, S. and McAllester, D.}}, pages = {{1130--1138}}, title = {{{Optimizing the F-measure in multi-label classification: Plug-in rule approach versus structured loss minimization}}}, year = {{2013}}, } @inproceedings{13117, author = {{Busa-Fekete, Robert and Szoreny, B. and Weng, P. and Cheng, W. and Hüllermeier, Eyke}}, booktitle = {{in Proceedings ICML-2013, 30th International Conference on Machine Learning, Atlanta, USA}}, editor = {{Dasgupta, S. and McAllester, D.}}, pages = {{1094--1102}}, title = {{{Top-k selection based on adaptive sampling of noisy preferences}}}, year = {{2013}}, } @inproceedings{13118, author = {{Hüllermeier, Eyke and Cheng, W.}}, booktitle = {{in Proceedings IJCAI-13, 23rd international Joint Conference on Artificial Intelligence, Beijing, China}}, editor = {{Rossi, F.}}, pages = {{3012--3016}}, publisher = {{AAAI Press}}, title = {{{Preference-based CBR: General ideas and basic principles}}}, year = {{2013}}, } @inproceedings{13119, author = {{Henzgen, Sascha and Strickert, M. and Hüllermeier, Eyke}}, booktitle = {{in Proceedings CORES 2013, 8th International Conference on Computer Recognition Systems, Wroclaw, Poland}}, editor = {{Burduk, R. and Jackowski, K. and Kurzynski, M. and Wozniak, M. and Zolnierek, A.}}, pages = {{279--288}}, publisher = {{Springer}}, title = {{{Rule chains for visualizing evolving fuzzy rule-based systems}}}, year = {{2013}}, } @inproceedings{13190, author = {{Shaker, Ammar and Hüllermeier, Eyke}}, booktitle = {{in Proceedings CORES 2013, 8th International Conference on Computer Recognition Systems, Wroclaw, Poland}}, editor = {{Burduk, R. and Jackowski, K. and Kurzynski, M. and Wozniak, W. and Zolnierek, A.}}, pages = {{289--298}}, publisher = {{Springer}}, title = {{{Recovery analysis for adaptive learning from non-stationary data streams}}}, year = {{2013}}, } @inproceedings{13645, author = {{Graf, Tobias and Schäfers, Lars and Platzner, Marco}}, booktitle = {{Proceedings of the International Conference on Computers and Games (CG)}}, publisher = {{Springer}}, title = {{{On Semeai Detection in Monte-Carlo Go.}}}, year = {{2013}}, } @inbook{46385, abstract = {{In many applications one is faced with the problem that multiple objectives have to be optimized at the same time. Since typically the solution set of such multi-objective optimization problems forms a manifold which cannot be computed analytically, one is in many cases interested in a suitable finite size approximation of this set. One widely used approach is to find a representative set that maximizes the dominated hypervolume that is defined by the images in objective space of these solutions and a given reference point. In this paper, we propose a new point-wise iterative search procedure, Hypervolume Directed Search (HVDS), that aims to increase the hypervolume of a given point in an archive for bi-objective unconstrained optimization problems. We present the HVDS both as a standalone algorithm and as a local searcher within a specialized evolutionary algorithm. Numerical results confirm the strength of the novel approach.}}, author = {{Sosa, Hernández V and Schütze, O and Rudolph, G and Trautmann, Heike}}, booktitle = {{EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation IV}}, editor = {{Emmerich, M and Deutz, A and Schuetze, O and Bäck, T and Tantar, A and Moral, PD and Legrand, P and Bouvry, P and Coello, CA}}, isbn = {{978-3-319-01127-1}}, pages = {{189–205}}, publisher = {{Springer International Publishing}}, title = {{{The Directed Search Method for Pareto Front Approximations with Maximum Dominated Hypervolume}}}, doi = {{10.1007/978-3-319-01128-8_13}}, volume = {{227}}, year = {{2013}}, } @inbook{46386, abstract = {{The averaged Hausdorff distance Δ p is a performance indicator in multi-objective evolutionary optimization which simultaneously takes into account proximity to the true Pareto front and uniform spread of solutions. Recently, the multi-objective evolutionary algorithm Δ p -EMOA was introduced which successfully generates evenly spaced Pareto front approximations for bi-objective problems by integrating an external archiving strategy into the SMS-EMOA based on Δ p . In this work a conceptual generalization of the Δ p -EMOA for higher objective space dimensions is presented and experimentally compared to state-of-the art EMOA as well as specialized EMOA variants on three-dimensional optimization problems.}}, author = {{Trautmann, Heike and Rudolph, G and Dominguez-Medina, C and Schütze, O}}, booktitle = {{EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation II}}, editor = {{Schütze, O and Coello, Coello CA and Tantar, A and Tantar, E and Bouvry, P and Del, Moral P and Legrand, P}}, isbn = {{978-3-642-31518-3}}, pages = {{89–105}}, publisher = {{Springer Berlin Heidelberg}}, title = {{{Finding Evenly Spaced Pareto Fronts for Three-Objective Optimization Problems}}}, doi = {{10.1007/978-3-642-31519-0_6}}, volume = {{175}}, year = {{2013}}, } @inproceedings{46388, abstract = {{Understanding the behaviour of well-known algorithms for classical NP-hard optimisation problems is still a difficult task. With this paper, we contribute to this research direction and carry out a feature based comparison of local search and the well-known Christofides approximation algorithm for the Traveling Salesperson Problem. We use an evolutionary algorithm approach to construct easy and hard instances for the Christofides algorithm, where we measure hardness in terms of approximation ratio. Our results point out important features and lead to hard and easy instances for this famous algorithm. Furthermore, our cross-comparison gives new insights on the complementary benefits of the different approaches.}}, author = {{Nallaperuma, Samadhi and Wagner, Markus and Neumann, Frank and Bischl, Bernd and Mersmann, Olaf and Trautmann, Heike}}, booktitle = {{Proceedings of the Twelfth Workshop on Foundations of Genetic Algorithms XII}}, isbn = {{9781450319904}}, keywords = {{approximation algorithms, local search, traveling salesperson problem, feature selection, prediction, classification}}, pages = {{147–160}}, publisher = {{Association for Computing Machinery}}, title = {{{A Feature-Based Comparison of Local Search and the Christofides Algorithm for the Travelling Salesperson Problem}}}, doi = {{10.1145/2460239.2460253}}, year = {{2013}}, } @inproceedings{46390, abstract = {{In some technical applications like multiobjective online control an evenly spaced approximation of the Pareto front is desired. Since standard evolutionary multiobjective optimization (EMO) algorithms have not been designed for that kind of approximation we propose an archive-based plug-in method that builds an evenly spaced approximation using averaged Hausdorff measure between archive and reference front. In case of three objectives this reference font is constructed from a triangulated approximation of the Pareto front from a previous experiment. The plug-in can be deployed in online or offline mode for any kind of EMO algorithm.}}, author = {{Rudolph, G and Trautmann, Heike and Sengupta, S and Schütze, O}}, booktitle = {{Evolutionary Multi-Criterion Optimization — 7$^th$ International Conference, EMO 2013, Sheffield, UK, Proceedings}}, editor = {{Purshouse, RC and Fleming, PJ and Fonseca, CM and Greco, S and Shaw, J}}, pages = {{443–458}}, publisher = {{Springer}}, title = {{{Evenly Spaced Pareto Front Approximations for Tricriteria Problems Based on Triangulation}}}, doi = {{https://doi.org/10.1007/978-3-642-37140-0_34}}, volume = {{7811}}, year = {{2013}}, } @inproceedings{46391, abstract = {{Indicator based evolutionary algorithms have caught the interest of many researchers for the treatment of multi-objective optimization problems in the recent past since they deliver the desired approximation of the solution set and due to a usually better performance compared to dominance based algorithms. Nevertheless, these methods still suffer the drawback that many function evaluations are required to obtain a suitable representation of the solution set. The aim of this study is to present the Directed Search (DS) Method as local searcher within global indicator based optimization algorithms. For this, we will present the DS in the context of hypervolume maximization leading to both a new local search algorithm and a new memetic algorithm. Further, we will present first attempts to adapt the DS to a class of parameter dependent problems.}}, author = {{Sosa-Hernandez, VA and Schütze, O and Rudoph, G and Trautmann, Heike}}, booktitle = {{Proceeding of the Fifteenth Annual Conference Companion on Genetic and Evolutionary Computation Conference Companion}}, pages = {{1699–1702}}, publisher = {{ACM}}, title = {{{Directed Search Method for Indicator-based Multi-objective Evolutionary Algorithms}}}, doi = {{10.1145/2464576.2482756}}, year = {{2013}}, } @inproceedings{46387, abstract = {{Here we address the problem of computing finite size Hausdorff approximations of the Pareto front of four-objective optimization problems by means of evolutionary computing. Since many applications desire an approximation evenly spread along the Pareto front and approximations that are good in the Hausdorff sense are typically evenly spread along the Pareto front we consider three different evolutionary multi-objective algorithms tailored to that purpose, where two of them are based on the Part and Selection Algorithm (PSA). Finally, we present some numerical results indicating the strength of the novel methods.}}, author = {{Dominguez-Medina, C and Rudolph, G and Schütze, O and Trautmann, Heike}}, booktitle = {{Proceedings of the 2013 IEEE Congress on Evolutionary Computation (CEC)}}, pages = {{3190–3197}}, title = {{{Evenly spaced Pareto fronts of quad-objective problems using PSA partitioning technique}}}, doi = {{https://doi.org/10.1109/CEC.2013.6557960}}, year = {{2013}}, } @inproceedings{46389, abstract = {{Current StarCraft bots are not very flexible in their strategy choice, most of them just follow a manually optimized one, usually a rush. We suggest a method of augmenting existing bots via Fuzzy Control in order to make them react on the current game situation. According to the available information, the best matching of a pool of strategies is chosen. While the method is very general and can be applied easily to many bots, we implement it for the existing BTHAI bot and show experimentally how the modifications affects its gameplay, and how it is improved compared to the original version.}}, author = {{Preuss, Mike and Kozakowski, Daniel and Hagelbäck, Johan and Trautmann, Heike}}, booktitle = {{2013 IEEE Conference on Computational Inteligence in Games (CIG)}}, pages = {{1--8}}, title = {{{Reactive strategy choice in StarCraft by means of Fuzzy Control}}}, doi = {{10.1109/CIG.2013.6633627}}, year = {{2013}}, } @article{46395, abstract = {{In multiobjective optimization, the identification of practically relevant solutions on the Pareto-optimal front is an important research topic. Desirability functions (DFs) allow the preferences of the decision maker to be specified in an intuitive way. Recently, it has been shown for continuous optimization problems that an a priori transformation of the objectives by means of DFs can be used to focus the search of a hypervolume-based evolutionary algorithm on the desired part of the front. In many-objective optimization, however, the computational complexity of the hypervolume can become a crucial part. Thus, an alternative to this approach will be presented in this paper. The new algorithm operates in the untransformed objective space, but the desirability index (DI), that is, a DF-based scalarization, will be used as the second-level selection criterion in the non-dominated sorting. The diversity and uniform distribution of the resulting approximation are ensured by the use of an external archive. In the experiments, different preferences are specified as DFs, and their effects are investigated. It is shown that trade-off solutions are generated in the desired regions of the Pareto-optimal front and with a density adaptive to the DI. The efficiency of the approach with respect to increasing objective space dimension is also analysed using scalable test functions. The convergence speed is superior to other set-based and preference-based evolutionary multiobjective algorithms while the approach is of low computational complexity due to cheap DI evaluations. Copyright © 2013 John Wiley & Sons, Ltd.}}, author = {{Trautmann, Heike and Wagner, T and Biermann, D and Weihs, C}}, journal = {{Journal of Multi-Criteria Decision Analysis}}, number = {{5-6}}, pages = {{319–337}}, title = {{{Indicator-based Selection in Evolutionary Multiobjective Optimization Algorithms Based On the Desirability Index}}}, doi = {{https://doi.org/10.1002/mcda.1503}}, volume = {{20}}, year = {{2013}}, } @inproceedings{46393, abstract = {{In multi-objective optimization, set-based performance indicators have become the state of the art for assessing the quality of Pareto front approximations. As a consequence, they are also more and more used within the design of multi-objective optimization algorithms. The R2 and the Hypervolume (HV) indicator represent two popular examples. In order to understand the behavior and the approximations preferred by these indicators and algorithms, a comprehensive knowledge of the indicator’s properties is required. Whereas this knowledge is available for the HV, we presented a first approach in this direction for the R2 indicator just recently. In this paper, we build upon this knowledge and enhance the considerations with respect to the integration of preferences into the R2 indicator. More specifically, we analyze the effect of the reference point, the domain of the weights, and the distribution of weight vectors on the optimization of $\mu$ solutions with respect to the R2 indicator. By means of theoretical findings and empirical evidence, we show the potentials of these three possibilities using the optimal distribution of $\mu$ solutions for exemplary setups.}}, author = {{Wagner, Tobias and Trautmann, Heike and Brockhoff, Dimo}}, booktitle = {{Evolutionary Multi-Criterion Optimization}}, editor = {{Purshouse, Robin C. and Fleming, Peter J. and Fonseca, Carlos M. and Greco, Salvatore and Shaw, Jane}}, isbn = {{978-3-642-37140-0}}, pages = {{81–95}}, publisher = {{Springer Berlin Heidelberg}}, title = {{{Preference Articulation by Means of the R2 Indicator}}}, year = {{2013}}, } @inproceedings{46392, abstract = {{An indicator-based evolutionary multiobjective optimization algorithm (EMOA) is introduced which incorporates the contribution to the unary R2-indicator as the secondary selection criterion. First experiments indicate that the R2-EMOA accurately approximates the Pareto front of the considered continuous multiobjective optimization problems. Furthermore, decision makers’ preferences can be included by adjusting the weight vector distributions of the indicator which results in a focused search behavior.}}, author = {{Trautmann, Heike and Wagner, Tobias and Brockhoff, Dimo}}, booktitle = {{Learning and Intelligent Optimization}}, editor = {{Nicosia, Giuseppe and Pardalos, Panos}}, isbn = {{978-3-642-44973-4}}, pages = {{70–74}}, publisher = {{Springer Berlin Heidelberg}}, title = {{{R2-EMOA: Focused Multiobjective Search Using R2-Indicator-Based Selection}}}, year = {{2013}}, } @article{48889, abstract = {{Meta-heuristics are frequently used to tackle NP-hard combinatorial optimization problems. With this paper we contribute to the understanding of the success of 2-opt based local search algorithms for solving the traveling salesperson problem (TSP). Although 2-opt is widely used in practice, it is hard to understand its success from a theoretical perspective. We take a statistical approach and examine the features of TSP instances that make the problem either hard or easy to solve. As a measure of problem difficulty for 2-opt we use the approximation ratio that it achieves on a given instance. Our investigations point out important features that make TSP instances hard or easy to be approximated by 2-opt.}}, author = {{Mersmann, Olaf and Bischl, Bernd and Trautmann, Heike and Wagner, Markus and Bossek, Jakob and Neumann, Frank}}, issn = {{1012-2443}}, journal = {{Annals of Mathematics and Artificial Intelligence}}, keywords = {{2-opt, 90B06, Classification, Feature selection, MARS, TSP}}, number = {{2}}, pages = {{151–182}}, title = {{{A Novel Feature-Based Approach to Characterize Algorithm Performance for the Traveling Salesperson Problem}}}, doi = {{10.1007/s10472-013-9341-2}}, volume = {{69}}, year = {{2013}}, } @inproceedings{47161, author = {{Fahl, Sascha and Harbach, Marian and Acar, Yasemin and Smith, Matthew}}, booktitle = {{Proceedings of the Ninth Symposium on Usable Privacy and Security}}, publisher = {{ACM}}, title = {{{On the ecological validity of a password study}}}, doi = {{10.1145/2501604.2501617}}, year = {{2013}}, }