@inproceedings{46369,
  abstract     = {{This paper formally defines multimodality in multiobjective optimization (MO). We introduce a test-bed in which multimodal MO problems with known properties can be constructed as well as numerical characteristics of the resulting landscape. Gradient- and local search based strategies are compared on exemplary problems together with specific performance indicators in the multimodal MO setting. By this means the foundation for Exploratory Landscape Analysis in MO is provided.}},
  author       = {{Kerschke, Pascal and Wang, Hao and Preuss, Mike and Grimme, Christian and Deutz, André and Trautmann, Heike and Emmerich, Michael}},
  booktitle    = {{Proceedings of the 14$^th$ International Conference on Parallel Problem Solving from Nature (PPSN XIV)}},
  pages        = {{962–972}},
  publisher    = {{Springer}},
  title        = {{{Towards Analyzing Multimodality of Multiobjective Landscapes}}},
  doi          = {{10.1007/978-3-319-45823-6_90}},
  year         = {{2016}},
}

@inproceedings{46367,
  abstract     = {{When selecting the best suited algorithm for an unknown optimization problem, it is useful to possess some a priori knowledge of the problem at hand. In the context of single-objective, continuous optimization problems such knowledge can be retrieved by means of Exploratory Landscape Analysis (ELA), which automatically identifies properties of a landscape, e.g., the so-called funnel structures, based on an initial sample. In this paper, we extract the relevant features (for detecting funnels) out of a large set of landscape features when only given a small initial sample consisting of 50 x D observations, where D is the number of decision space dimensions. This is already in the range of the start population sizes of many evolutionary algorithms. The new Multiple Peaks Model Generator (MPM2) is used for training the classifier, and the approach is then very successfully validated on the Black-Box Optimization Benchmark (BBOB) and a subset of the CEC 2013 niching competition problems.}},
  author       = {{Kerschke, Pascal and Preuss, Mike and Wessing, Simon and Trautmann, Heike}},
  booktitle    = {{Proceedings of the 18$^th$ Annual Conference on Genetic and Evolutionary Computation}},
  isbn         = {{978-1-4503-4206-3}},
  pages        = {{229–236}},
  title        = {{{Low-Budget Exploratory Landscape Analysis on Multiple Peaks Models}}},
  doi          = {{10.1145/2908812.2908845}},
  year         = {{2016}},
}

@article{46371,
  abstract     = {{One main task in evolutionary multiobjective optimization (EMO) is to obtain a suitable finite size approximation of the Pareto front which is the image of the solution set, termed the Pareto set, of a given multiobjective optimization problem. In the technical literature, the characteristic of the desired approximation is commonly expressed by closeness to the Pareto front and a sufficient spread of the solutions obtained. In this paper, we first make an effort to show by theoretical and empirical findings that the recently proposed Averaged Hausdorff (or Δ𝑝-) indicator indeed aims at fulfilling both performance criteria for bi-objective optimization problems. In the second part of this paper, standard EMO algorithms combined with a specialized archiver and a postprocessing step based on the Δ𝑝 indicator are introduced which sufficiently approximate the Δ𝑝-optimal archives and generate solutions evenly spread along the Pareto front.}},
  author       = {{Rudolph, G and Schütze, O and Grimme, C and Domínguez-Medina, C and Trautmann, Heike}},
  journal      = {{Computational Optimization and Applications (Comput. Optim. Appl.)}},
  number       = {{2}},
  pages        = {{589–618}},
  title        = {{{Optimal averaged Hausdorff archives for bi-objective problems: theoretical and numerical results}}},
  doi          = {{10.1007/s10589-015-9815-8}},
  volume       = {{64}},
  year         = {{2016}},
}

@article{46372,
  abstract     = {{We present a new hybrid evolutionary algorithm for the effective hypervolume approximation of the Pareto front of a given differentiable multi-objective optimization problem. Starting point for the local search (LS) mechanism is a new division of the decision space as we will argue that in each of these regions a different LS strategy seems to be most promising. For the LS in two out of the three regions we will utilize and adapt the Directed Search method which is capable of steering the search into any direction given in objective space and which is thus well suited for the problem at hand. We further on integrate the resulting LS mechanism into SMS-EMOA, a state-of-the-art evolutionary algorithm for hypervolume approximations. Finally, we will present some numerical results on several benchmark problems with two and three objectives indicating the strength and competitiveness of the novel hybrid.}},
  author       = {{Schütze, O and Sosa, Hernandez VA and Trautmann, Heike and Rudolph, G}},
  journal      = {{Journal of Heuristics}},
  number       = {{3}},
  pages        = {{273–300}},
  title        = {{{The Hypervolume based Directed Search Method for Multi-Objective Optimization Problems}}},
  doi          = {{10.1007/s10732-016-9310-0}},
  volume       = {{22}},
  year         = {{2016}},
}

@inproceedings{46368,
  abstract     = {{Exploratory Landscape Analysis (ELA) aims at understanding characteristics of single-objective continuous (black-box) optimization problems in an automated way. Moreover, the approach provides the basis for constructing algorithm selection models for unseen problem instances. Recently, it has gained increasing attention and numerical features have been designed by various research groups. This paper introduces the R-Package FLACCO which makes all relevant features available in a unified framework together with efficient helper functions. Moreover, a case study which gives perspectives to ELA for multi-objective optimization problems is presented.}},
  author       = {{Kerschke, Pascal and Trautmann, Heike}},
  booktitle    = {{Proceedings of the IEEE Congress on Evolutionary Computation (CEC)}},
  title        = {{{The R-Package FLACCO for Exploratory Landscape Analysis with Applications to Multi-Objective Optimization Problems}}},
  doi          = {{10.1109/CEC.2016.7748359}},
  year         = {{2016}},
}

@article{46370,
  abstract     = {{This report documents the talks and discussions at the Dagstuhl Seminar 15211 "Theory of Evolutionary Algorithms". This seminar, now in its 8th edition, is the main meeting point of the highly active theory of randomized search heuristics subcommunities in Australia, Asia, North America, and Europe. Topics intensively discussed include rigorous runtime analysis and computational complexity theory for randomised search heuristics, information geometry of randomised search, and synergies between the theory of evolutionary algorithms and theories of natural evolution.}},
  author       = {{Neumann, F and Trautmann, Heike}},
  journal      = {{Dagstuhl Reports}},
  number       = {{5}},
  pages        = {{78–79}},
  title        = {{{Working Group Report: Bridging the Gap Between Experiments and Theory Using Feature-Based Run-Time Analysis; Theory of Evolutionary Algorithms (Dagstuhl Seminar 15211)}}},
  doi          = {{10.4230/DagRep.5.5.57}},
  volume       = {{5}},
  year         = {{2016}},
}

@inproceedings{48309,
  author       = {{Habernal, Ivan and Gurevych, Iryna}},
  booktitle    = {{Proceedings of the 2016 Conference on Empirical Methods in Natural          Language Processing}},
  publisher    = {{Association for Computational Linguistics}},
  title        = {{{What makes a convincing argument? Empirical analysis and detecting            attributes of convincingness in Web argumentation}}},
  doi          = {{10.18653/v1/d16-1129}},
  year         = {{2016}},
}

@inproceedings{48873,
  abstract     = {{Despite the intrinsic hardness of the Traveling Salesperson Problem (TSP) heuristic solvers, e.g., LKH+restart and EAX+restart, are remarkably successful in generating satisfactory or even optimal solutions. However, the reasons for their success are not yet fully understood. Recent approaches take an analytical viewpoint and try to identify instance features, which make an instance hard or easy to solve. We contribute to this area by generating instance sets for couples of TSP algorithms A and B by maximizing/minimizing their performance difference in order to generate instances which are easier to solve for one solver and much harder to solve for the other. This instance set offers the potential to identify key features which allow to distinguish between the problem hardness classes of both algorithms.}},
  author       = {{Bossek, Jakob and Trautmann, Heike}},
  booktitle    = {{Learning and Intelligent Optimization}},
  editor       = {{Festa, Paola and Sellmann, Meinolf and Vanschoren, Joaquin}},
  isbn         = {{978-3-319-50349-3}},
  keywords     = {{Algorithm selection, Feature selection, Instance hardness, TSP}},
  pages        = {{48–59}},
  publisher    = {{Springer International Publishing}},
  title        = {{{Evolving Instances for Maximizing Performance Differences of State-of-the-Art Inexact TSP Solvers}}},
  doi          = {{10.1007/978-3-319-50349-3_4}},
  year         = {{2016}},
}

@article{52803,
  author       = {{Borgwardt, Stefan and Mailis, Theofilos and Peñaloza, Rafael and Turhan, Anni-Yasmin}},
  issn         = {{1861-2032}},
  journal      = {{Journal on Data Semantics}},
  keywords     = {{Artificial Intelligence, Computer Networks and Communications, Information Systems}},
  number       = {{2}},
  pages        = {{55--75}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Answering Fuzzy Conjunctive Queries Over Finitely Valued Fuzzy Ontologies}}},
  doi          = {{10.1007/s13740-015-0055-y}},
  volume       = {{5}},
  year         = {{2016}},
}

@inproceedings{47240,
  author       = {{Acar, Yasemin and Backes, Michael and Bugiel, Sven and Fahl, Sascha and McDaniel, Patrick and Smith, Matthew}},
  booktitle    = {{2016 IEEE Symposium on Security and Privacy (SP)}},
  publisher    = {{IEEE}},
  title        = {{{SoK: Lessons Learned from Android Security Research for Appified Software Platforms}}},
  doi          = {{10.1109/sp.2016.33}},
  year         = {{2016}},
}

@inproceedings{47239,
  author       = {{Dechand, Sergej and Schürmann, Dominik and Busse, Karoline and Acar, Yasemin and Fahl, Sascha and Smith, Matthew}},
  booktitle    = {{25th USENIX Security Symposium, USENIX Security 16, Austin, TX, USA, August 10-12, 2016}},
  editor       = {{Holz, Thorsten and Savage, Stefan}},
  pages        = {{193–208}},
  publisher    = {{USENIX Association}},
  title        = {{{An Empirical Study of Textual Key-Fingerprint Representations}}},
  year         = {{2016}},
}

@inproceedings{47241,
  author       = {{Acar, Yasemin and Backes, Michael and Fahl, Sascha and Kim, Doowon and Mazurek, Michelle L. and Stransky, Christian}},
  booktitle    = {{2016 IEEE Symposium on Security and Privacy (SP)}},
  publisher    = {{IEEE}},
  title        = {{{You Get Where You're Looking for: The Impact of Information Sources on Code Security}}},
  doi          = {{10.1109/sp.2016.25}},
  year         = {{2016}},
}

@inproceedings{46365,
  abstract     = {{Despite the intrinsic hardness of the Traveling Salesperson Problem (TSP) heuristic solvers, e.g., LKH+restart and EAX+restart, are remarkably successful in generating satisfactory or even optimal solutions. However, the reasons for their success are not yet fully understood. Recent approaches take an analytical viewpoint and try to identify instance features, which make an instance hard or easy to solve. We contribute to this area by generating instance sets for couples of TSP algorithms A and B by maximizing/minimizing their performance difference in order to generate instances which are easier to solve for one solver and much harder to solve for the other. This instance set offers the potential to identify key features which allow to distinguish between the problem hardness classes of both algorithms.}},
  author       = {{Bossek, Jakob and Trautmann, Heike}},
  booktitle    = {{Learning and Intelligent Optimization}},
  editor       = {{Festa, P and Sellmann, M and Vanschoren, J}},
  isbn         = {{978-3-319-50348-6}},
  pages        = {{48–59}},
  publisher    = {{Springer International Publishing}},
  title        = {{{Evolving Instances for Maximizing Performance Differences of State-of-The-Art Inexact TSP Solvers}}},
  doi          = {{10.1007/978-3-319-50349-3_4}},
  volume       = {{10079}},
  year         = {{2016}},
}

@inproceedings{46366,
  abstract     = {{State of the Art inexact solvers of the NP-hard Traveling Salesperson Problem (TSP) are known to mostly yield high-quality solutions in reasonable computation times. With the purpose of understanding different levels of instance difficulties, instances for the current State of the Art heuristic TSP solvers LKH+restart and EAX+restart are presented which are evolved using a sophisticated evolutionary algorithm. More specifically, the performance differences of the respective solvers are maximized resulting in instances which are easier to solve for one solver and much more difficult for the other. Focusing on both optimization directions, instance features are identified which characterize both types of instances and increase the understanding of solver performance differences.}},
  author       = {{Bossek, Jakob and Trautmann, Heike}},
  booktitle    = {{AI*IA 2016 Advances in Artificial Intelligence}},
  editor       = {{Adorni, G and Cagnoni, S and Gori, M and Maratea, M}},
  isbn         = {{978-3-319-49129-5}},
  pages        = {{3–12}},
  publisher    = {{Springer}},
  title        = {{{Understanding Characteristics of Evolved Instances for State-of-the-Art Inexact TSP Solvers with Maximum Performance Difference}}},
  doi          = {{10.1007/978-3-319-49130-1_1}},
  volume       = {{10037}},
  year         = {{2016}},
}

@inproceedings{8159,
  abstract     = {{The Boolean constraint satisfaction problem 3-SAT is arguably the canonical NP-complete problem. In contrast, 2-SAT can not only be decided in polynomial time, but in fact in deterministic linear time. In 2006, Bravyi proposed a physically motivated generalization of k-SAT to the quantum setting, defining the problem "quantum k-SAT". He showed that quantum 2-SAT is also solvable in polynomial time on a classical computer, in particular in deterministic time O(n^4), assuming unit-cost arithmetic over a field extension of the rational numbers, where n is number of variables. In this paper, we present an algorithm for quantum 2-SAT which runs in linear time, i.e. deterministic time O(n+m) for n and m the number of variables and clauses, respectively. Our approach exploits the transfer matrix techniques of Laumann et al. [QIC, 2010] used in the study of phase transitions for random quantum 2-SAT, and bears similarities with both the linear time 2-SAT algorithms of Even, Itai, and Shamir (based on backtracking) [SICOMP, 1976] and Aspvall, Plass, and Tarjan (based on strongly connected components) [IPL, 1979].}},
  author       = {{de Beaudrap, Niel and Gharibian, Sevag}},
  booktitle    = {{Proceedings of the 31st Conference on Computational Complexity (CCC 2016)}},
  editor       = {{Raz, Ran}},
  isbn         = {{978-3-95977-008-8}},
  keywords     = {{quantum 2-SAT, transfer matrix, strongly connected components, limited backtracking, local Hamiltonian}},
  location     = {{Tokyo, Japan}},
  pages        = {{27:1--17:21}},
  publisher    = {{Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik}},
  title        = {{{A Linear Time Algorithm for Quantum 2-SAT}}},
  doi          = {{10.4230/LIPIcs.CCC.2016.27}},
  volume       = {{50}},
  year         = {{2016}},
}

@inbook{29,
  abstract     = {{In this chapter, we present an introduction to the ReconOS operating system for reconfigurable computing. ReconOS offers a unified multi-threaded programming model and operating system services for threads executing in software and threads mapped to reconfigurable hardware. By supporting standard POSIX operating system functions for both software and hardware threads, ReconOS particularly caters to developers with a software background, because developers can use well-known mechanisms such as semaphores, mutexes, condition variables, and message queues for developing hybrid applications with threads running on the CPU and FPGA concurrently. Through the semantic integration of hardware accelerators into a standard operating system environment, ReconOS allows for rapid design space exploration, supports a structured application development process and improves the portability of applications between different reconfigurable computing systems.}},
  author       = {{Agne, Andreas and Platzner, Marco and Plessl, Christian and Happe, Markus and Lübbers, Enno}},
  booktitle    = {{FPGAs for Software Programmers}},
  editor       = {{Koch, Dirk and Hannig, Frank and Ziener, Daniel}},
  isbn         = {{978-3-319-26406-6}},
  pages        = {{227--244}},
  publisher    = {{Springer International Publishing}},
  title        = {{{ReconOS}}},
  doi          = {{10.1007/978-3-319-26408-0_13}},
  year         = {{2016}},
}

@inproceedings{31,
  author       = {{Riebler, Heinrich and Vaz, Gavin Francis and Plessl, Christian and Trainiti, Ettore M. G. and Durelli, Gianluca C. and Bolchini, Cristiana}},
  booktitle    = {{Proc. HiPEAC Workshop on Reonfigurable Computing (WRC)}},
  title        = {{{Using Just-in-Time Code Generation for Transparent Resource Management in Heterogeneous Systems}}},
  year         = {{2016}},
}

@inproceedings{24,
  author       = {{Kenter, Tobias and Plessl, Christian}},
  booktitle    = {{Proc. Workshop on Heterogeneous High-performance Reconfigurable Computing (H2RC)}},
  title        = {{{Microdisk Cavity FDTD Simulation on FPGA using OpenCL}}},
  year         = {{2016}},
}

@inproceedings{25,
  author       = {{Lass, Michael and Kühne, Thomas and Plessl, Christian}},
  booktitle    = {{Workshop on Approximate Computing (AC)}},
  title        = {{{Using Approximate Computing in Scientific Codes}}},
  year         = {{2016}},
}

@inproceedings{138,
  abstract     = {{Hardware accelerators are becoming popular in academia and industry. To move one step further from the state-of-the-art multicore plus accelerator approaches, we present in this paper our innovative SAVEHSA architecture. It comprises of a heterogeneous hardware platform with three different high-end accelerators attached over PCIe (GPGPU, FPGA and Intel MIC). Such systems can process parallel workloads very efficiently whilst being more energy efficient than regular CPU systems. To leverage the heterogeneity, the workload has to be distributed among the computing units in a way that each unit is well-suited for the assigned task and executable code must be available. To tackle this problem we present two software components; the first can perform resource allocation at runtime while respecting system and application goals (in terms of throughput, energy, latency, etc.) and the second is able to analyze an application and generate executable code for an accelerator at runtime. We demonstrate the first proof-of-concept implementation of our framework on the heterogeneous platform, discuss different runtime policies and measure the introduced overheads.}},
  author       = {{Riebler, Heinrich and Vaz, Gavin Francis and Plessl, Christian and Trainiti, Ettore M. G.  and Durelli, Gianluca C. and Del Sozzo, Emanuele and Santambrogio, Marco D.  and Bolchini, Christina}},
  booktitle    = {{Proceedings of International Forum on Research and Technologies for Society and Industry (RTSI)}},
  pages        = {{1--5}},
  publisher    = {{IEEE}},
  title        = {{{Using Just-in-Time Code Generation for Transparent Resource Management in Heterogeneous Systems}}},
  doi          = {{10.1109/RTSI.2016.7740545}},
  year         = {{2016}},
}