@inproceedings{48845, abstract = {{In practice, e.g. in delivery and service scenarios, Vehicle-Routing-Problems (VRPs) often imply repeated decision making on dynamic customer requests. As in classical VRPs, tours have to be planned short while the number of serviced customers has to be maximized at the same time resulting in a multi-objective problem. Beyond that, however, dynamic requests lead to the need for re-planning of not yet realized tour parts, while already realized tour parts are irreversible. In this paper we study this type of bi-objective dynamic VRP including sequential decision making and concurrent realization of decisions. We adopt a recently proposed Dynamic Evolutionary Multi-Objective Algorithm (DEMOA) for a related VRP problem and extend it to the more realistic (here considered) scenario of multiple vehicles. We empirically show that our DEMOA is competitive with a multi-vehicle offline and clairvoyant variant of the proposed DEMOA as well as with the dynamic single-vehicle approach proposed earlier.}}, author = {{Bossek, Jakob and Grimme, Christian and Trautmann, Heike}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference}}, isbn = {{978-1-4503-7128-5}}, keywords = {{decision making, dynamic optimization, evolutionary algorithms, multi-objective optimization, vehicle routing}}, pages = {{166–174}}, publisher = {{Association for Computing Machinery}}, title = {{{Dynamic Bi-Objective Routing of Multiple Vehicles}}}, doi = {{10.1145/3377930.3390146}}, year = {{2020}}, } @inproceedings{48852, abstract = {{The Traveling Salesperson Problem (TSP) is one of the best-known combinatorial optimisation problems. However, many real-world problems are composed of several interacting components. The Traveling Thief Problem (TTP) addresses such interactions by combining two combinatorial optimisation problems, namely the TSP and the Knapsack Problem (KP). Recently, a new problem called the node weight dependent Traveling Salesperson Problem (W-TSP) has been introduced where nodes have weights that influence the cost of the tour. In this paper, we compare W-TSP and TTP. We investigate the structure of the optimised tours for W-TSP and TTP and the impact of using each others fitness function. Our experimental results suggest (1) that the W-TSP often can be solved better using the TTP fitness function and (2) final W-TSP and TTP solutions show different distributions when compared with optimal TSP or weighted greedy solutions.}}, author = {{Bossek, Jakob and Neumann, Aneta and Neumann, Frank}}, booktitle = {{Parallel Problem Solving from Nature (PPSN XVI)}}, isbn = {{978-3-030-58111-4}}, keywords = {{Evolutionary algorithms, Node weight dependent TSP, Traveling Thief Problem}}, pages = {{346–359}}, publisher = {{Springer-Verlag}}, title = {{{Optimising Tours for the Weighted Traveling Salesperson Problem and the Traveling Thief Problem: A Structural Comparison of Solutions}}}, doi = {{10.1007/978-3-030-58112-1_24}}, year = {{2020}}, } @inproceedings{48879, abstract = {{Evolving diverse sets of high quality solutions has gained increasing interest in the evolutionary computation literature in recent years. With this paper, we contribute to this area of research by examining evolutionary diversity optimisation approaches for the classical Traveling Salesperson Problem (TSP). We study the impact of using different diversity measures for a given set of tours and the ability of evolutionary algorithms to obtain a diverse set of high quality solutions when adopting these measures. Our studies show that a large variety of diverse high quality tours can be achieved by using our approaches. Furthermore, we compare our approaches in terms of theoretical properties and the final set of tours obtained by the evolutionary diversity optimisation algorithm.}}, author = {{Do, Anh Viet and Bossek, Jakob and Neumann, Aneta and Neumann, Frank}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference}}, isbn = {{978-1-4503-7128-5}}, keywords = {{diversity maximisation, evolutionary algorithms, travelling salesperson problem}}, pages = {{681–689}}, publisher = {{Association for Computing Machinery}}, title = {{{Evolving Diverse Sets of Tours for the Travelling Salesperson Problem}}}, doi = {{10.1145/3377930.3389844}}, year = {{2020}}, } @inproceedings{48895, abstract = {{Evolutionary algorithms (EAs) are general-purpose problem solvers that usually perform an unbiased search. This is reasonable and desirable in a black-box scenario. For combinatorial optimization problems, often more knowledge about the structure of optimal solutions is given, which can be leveraged by means of biased search operators. We consider the Minimum Spanning Tree (MST) problem in a single- and multi-objective version, and introduce a biased mutation, which puts more emphasis on the selection of edges of low rank in terms of low domination number. We present example graphs where the biased mutation can significantly speed up the expected runtime until (Pareto-)optimal solutions are found. On the other hand, we demonstrate that bias can lead to exponential runtime if "heavy" edges are necessarily part of an optimal solution. However, on general graphs in the single-objective setting, we show that a combined mutation operator which decides for unbiased or biased edge selection in each step with equal probability exhibits a polynomial upper bound - as unbiased mutation - in the worst case and benefits from bias if the circumstances are favorable.}}, author = {{Roostapour, Vahid and Bossek, Jakob and Neumann, Frank}}, booktitle = {{Proceedings of the 2020 Genetic and Evolutionary Computation Conference}}, isbn = {{978-1-4503-7128-5}}, keywords = {{biased mutation, evolutionary algorithms, minimum spanning tree problem, runtime analysis}}, pages = {{551–559}}, publisher = {{Association for Computing Machinery}}, title = {{{Runtime Analysis of Evolutionary Algorithms with Biased Mutation for the Multi-Objective Minimum Spanning Tree Problem}}}, doi = {{10.1145/3377930.3390168}}, year = {{2020}}, } @inproceedings{17667, abstract = {{Resolving distributed attacks benefits from collaboration between networks. We present three approaches for the same multi-domain defensive action that can be applied in such an alliance: 1) Counteract Everywhere, 2) Minimize Countermeasures, and 3) Minimize Propagation. First, we provide a formula to compute efficiency of a defense; then we use this formula to compute the efficiency of the approaches under various circumstances. Finally, we discuss how task execution order and timing influence defense efficiency. Our results show that the Minimize Propagation approach is the most efficient method when defending against the chosen attack.}}, author = {{Koning, Ralph and Polevoy, Gleb and Meijer, Lydia and de Laat, Cees and Grosso, Paola}}, booktitle = {{2019 6th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/ 2019 5th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom)}}, issn = {{null}}, keywords = {{computer network security, multinetwork environments, multidomain defensive action, task execution order, timing influence defense efficiency, distributed attacks, collaborative security defence approach, minimize propagation approach, minimize countermeasure approach, counteract everywhere approach, Conferences, Cloud computing, Computer crime, Edge computing, Security, Defense Approaches, Multi-Domain Defense, Collaborative Defense, Defense Algorithms, Computer Networks}}, pages = {{113--123}}, title = {{{Approaches for Collaborative Security Defences in Multi Network Environments}}}, doi = {{10.1109/CSCloud/EdgeCom.2019.000-9}}, year = {{2019}}, } @inproceedings{10586, abstract = {{We consider the problem of transforming a given graph G_s into a desired graph G_t by applying a minimum number of primitives from a particular set of local graph transformation primitives. These primitives are local in the sense that each node can apply them based on local knowledge and by affecting only its 1-neighborhood. Although the specific set of primitives we consider makes it possible to transform any (weakly) connected graph into any other (weakly) connected graph consisting of the same nodes, they cannot disconnect the graph or introduce new nodes into the graph, making them ideal in the context of supervised overlay network transformations. We prove that computing a minimum sequence of primitive applications (even centralized) for arbitrary G_s and G_t is NP-hard, which we conjecture to hold for any set of local graph transformation primitives satisfying the aforementioned properties. On the other hand, we show that this problem admits a polynomial time algorithm with a constant approximation ratio.}}, author = {{Scheideler, Christian and Setzer, Alexander}}, booktitle = {{Proceedings of the 46th International Colloquium on Automata, Languages, and Programming}}, keywords = {{Graphs transformations, NP-hardness, approximation algorithms}}, location = {{Patras, Greece}}, pages = {{150:1----150:14}}, publisher = {{Dagstuhl Publishing}}, title = {{{On the Complexity of Local Graph Transformations}}}, doi = {{10.4230/LIPICS.ICALP.2019.150}}, volume = {{132}}, year = {{2019}}, } @inproceedings{48843, abstract = {{We contribute to the theoretical understanding of randomized search heuristics for dynamic problems. We consider the classical graph coloring problem and investigate the dynamic setting where edges are added to the current graph. We then analyze the expected time for randomized search heuristics to recompute high quality solutions. This includes the (1+1) EA and RLS in a setting where the number of colors is bounded and we are minimizing the number of conflicts as well as iterated local search algorithms that use an unbounded color palette and aim to use the smallest colors and - as a consequence - the smallest number of colors. We identify classes of bipartite graphs where reoptimization is as hard as or even harder than optimization from scratch, i. e. starting with a random initialization. Even adding a single edge can lead to hard symmetry problems. However, graph classes that are hard for one algorithm turn out to be easy for others. In most cases our bounds show that reoptimization is faster than optimizing from scratch. Furthermore, we show how to speed up computations by using problem specific operators concentrating on parts of the graph where changes have occurred.}}, author = {{Bossek, Jakob and Neumann, Frank and Peng, Pan and Sudholt, Dirk}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference}}, isbn = {{978-1-4503-6111-8}}, keywords = {{dynamic optimization, evolutionary algorithms, running time analysis, theory}}, pages = {{1443–1451}}, publisher = {{Association for Computing Machinery}}, title = {{{Runtime Analysis of Randomized Search Heuristics for Dynamic Graph Coloring}}}, doi = {{10.1145/3321707.3321792}}, year = {{2019}}, } @inproceedings{2367, abstract = {{One of the most popular fuzzy clustering techniques is the fuzzy K-means algorithm (also known as fuzzy-c-means or FCM algorithm). In contrast to the K-means and K-median problem, the underlying fuzzy K-means problem has not been studied from a theoretical point of view. In particular, there are no algorithms with approximation guarantees similar to the famous K-means++ algorithm known for the fuzzy K-means problem. This work initiates the study of the fuzzy K-means problem from an algorithmic and complexity theoretic perspective. We show that optimal solutions for the fuzzy K-means problem cannot, in general, be expressed by radicals over the input points. Surprisingly, this already holds for simple inputs in one-dimensional space. Hence, one cannot expect to compute optimal solutions exactly. We give the first (1+eps)-approximation algorithms for the fuzzy K-means problem. First, we present a deterministic approximation algorithm whose runtime is polynomial in N and linear in the dimension D of the input set, given that K is constant, i.e. a polynomial time approximation scheme (PTAS) for fixed K. We achieve this result by showing that for each soft clustering there exists a hard clustering with similar properties. Second, by using techniques known from coreset constructions for the K-means problem, we develop a deterministic approximation algorithm that runs in time almost linear in N but exponential in the dimension D. We complement these results with a randomized algorithm which imposes some natural restrictions on the sought solution and whose runtime is comparable to some of the most efficient approximation algorithms for K-means, i.e. linear in the number of points and the dimension, but exponential in the number of clusters.}}, author = {{Blömer, Johannes and Brauer, Sascha and Bujna, Kathrin}}, booktitle = {{2016 IEEE 16th International Conference on Data Mining (ICDM)}}, isbn = {{9781509054732}}, keywords = {{unsolvability by radicals, clustering, fuzzy k-means, probabilistic method, approximation algorithms, randomized algorithms}}, location = {{Barcelona, Spain}}, pages = {{805--810}}, publisher = {{IEEE}}, title = {{{A Theoretical Analysis of the Fuzzy K-Means Problem}}}, doi = {{10.1109/icdm.2016.0094}}, year = {{2016}}, } @article{17657, abstract = {{Inter-datacenter transfers of non-interactive but timely large flows over a private (managed) network is an important problem faced by many cloud service providers. The considered flows are non-interactive because they do not explicitly target the end users. However, most of them must be performed on a timely basis and are associated with a deadline. We propose to schedule these flows by a centralized controller, which determines when to transmit each flow and which path to use. Two scheduling models are presented in this paper. In the first, the controller also determines the rate of each flow, while in the second bandwidth is assigned by the network according to the TCP rules. We develop scheduling algorithms for both models and compare their complexity and performance.}}, author = {{Cohen, R. and Polevoy, Gleb}}, issn = {{2168-7161}}, journal = {{Cloud Computing, IEEE Transactions on}}, keywords = {{Approximation algorithms, Approximation methods, Bandwidth, Cloud computing, Routing, Schedules, Scheduling}}, number = {{99}}, pages = {{1--1}}, title = {{{Inter-Datacenter Scheduling of Large Data Flows}}}, doi = {{10.1109/TCC.2015.2487964}}, volume = {{PP}}, year = {{2015}}, } @inproceedings{5678, abstract = {{Many academic disciplines - including information systems, computer science, and operations management - face scheduling problems as important decision making tasks. Since many scheduling problems are NP-hard in the strong sense, there is a need for developing solution heuristics. For scheduling problems with setup times on unrelated parallel machines, there is limited research on solution methods and to the best of our knowledge, parallel computer architectures have not yet been taken advantage of. We address this gap by proposing and implementing a new solution heuristic and by testing different parallelization strategies. In our computational experiments, we show that our heuristic calculates near-optimal solutions even for large instances and that computing time can be reduced substantially by our parallelization approach.}}, author = {{Rauchecker, Gerhard and Schryen, Guido}}, booktitle = {{Australasian Conference on Information Systems}}, keywords = {{scheduling, decision support, heuristic, high performance computing, parallel algorithms}}, pages = {{1--13}}, title = {{{High-Performance Computing for Scheduling Decision Support: A Parallel Depth-First Search Heuristic}}}, year = {{2015}}, } @inproceedings{48838, abstract = {{The majority of algorithms can be controlled or adjusted by parameters. Their values can substantially affect the algorithms’ performance. Since the manual exploration of the parameter space is tedious – even for few parameters – several automatic procedures for parameter tuning have been proposed. Recent approaches also take into account some characteristic properties of the problem instances, frequently termed instance features. Our contribution is the proposal of a novel concept for feature-based algorithm parameter tuning, which applies an approximating surrogate model for learning the continuous feature-parameter mapping. To accomplish this, we learn a joint model of the algorithm performance based on both the algorithm parameters and the instance features. The required data is gathered using a recently proposed acquisition function for model refinement in surrogate-based optimization: the profile expected improvement. This function provides an avenue for maximizing the information required for the feature-parameter mapping, i.e., the mapping from instance features to the corresponding optimal algorithm parameters. The approach is validated by applying the tuner to exemplary evolutionary algorithms and problems, for which theoretically grounded or heuristically determined feature-parameter mappings are available.}}, author = {{Bossek, Jakob and Bischl, Bernd and Wagner, Tobias and Rudolph, Günter}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference}}, isbn = {{978-1-4503-3472-3}}, keywords = {{evolutionary algorithms, model-based optimization, parameter tuning}}, pages = {{1319–1326}}, publisher = {{Association for Computing Machinery}}, title = {{{Learning Feature-Parameter Mappings for Parameter Tuning via the Profile Expected Improvement}}}, doi = {{10.1145/2739480.2754673}}, year = {{2015}}, } @inproceedings{48887, abstract = {{We evaluate the performance of a multi-objective evolutionary algorithm on a class of dynamic routing problems with a single vehicle. In particular we focus on relating algorithmic performance to the most prominent characteristics of problem instances. The routing problem considers two types of customers: mandatory customers must be visited whereas optional customers do not necessarily have to be visited. Moreover, mandatory customers are known prior to the start of the tour whereas optional customers request for service at later points in time with the vehicle already being on its way. The multi-objective optimization problem then results as maximizing the number of visited customers while simultaneously minimizing total travel time. As an a-posteriori evaluation tool, the evolutionary algorithm aims at approximating the related Pareto set for specifically designed benchmarking instances differing in terms of number of customers, geographical layout, fraction of mandatory customers, and request times of optional customers. Conceptional and experimental comparisons to online heuristic procedures are provided.}}, author = {{Meisel, Stephan and Grimme, Christian and Bossek, Jakob and Wölck, Martin and Rudolph, Günter and Trautmann, Heike}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference }}, isbn = {{978-1-4503-3472-3}}, keywords = {{combinatorial optimization, metaheuristics, multi-objective optimization, online algorithms, transportation}}, pages = {{425–432}}, publisher = {{Association for Computing Machinery}}, title = {{{Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic Routing of a Vehicle}}}, doi = {{10.1145/2739480.2754705}}, year = {{2015}}, } @inproceedings{9889, abstract = {{A measurement method is presented that combines the advantages of the multisine measurement technique with a prediction method for peak bending behavior. This combination allows the analysis of the dynamic behavior of mechanical structures at distinctly reduced measurement durations and has the advantage of reducing high excitation impacts on the structure under test.}}, author = {{Sprock, Christian and Sextro, Walter}}, booktitle = {{Instrumentation and Measurement Technology Conference (I2MTC) Proceedings, 2014 IEEE International}}, keywords = {{bending, dynamic testing, measurement, structural engineering, vibrations, measurement durations, mechanical structures, multisine measurement technique, nonlinear peak bending behavior, prediction method, time-efficient dynamic analysis, Heuristic algorithms, Nonlinear systems, Oscillators, Time measurement, Time-frequency analysis, Vibrations}}, pages = {{320--324}}, title = {{{Time-efficient dynamic analysis of structures exhibiting nonlinear peak bending}}}, doi = {{10.1109/I2MTC.2014.6860760}}, year = {{2014}}, } @article{17663, abstract = {{In this paper, we define and study a new problem, referred to as the Dependent Unsplittable Flow Problem (D-UFP). We present and discuss this problem in the context of large-scale powerful (radar/camera) sensor networks, but we believe it has important applications on the admission of large flows in other networks as well. In order to optimize the selection of flows transmitted to the gateway, D-UFP takes into account possible dependencies between flows. We show that D-UFP is more difficult than NP-hard problems for which no good approximation is known. Then, we address two special cases of this problem: the case where all the sensors have a shared channel and the case where the sensors form a mesh and route to the gateway over a spanning tree.}}, author = {{Cohen, R. and Nudelman, I. and Polevoy, Gleb}}, issn = {{1063-6692}}, journal = {{Networking, IEEE/ACM Transactions on}}, keywords = {{Approximation algorithms, Approximation methods, Bandwidth, Logic gates, Radar, Vectors, Wireless sensor networks, Dependent flow scheduling, sensor networks}}, number = {{5}}, pages = {{1461--1471}}, title = {{{On the Admission of Dependent Flows in Powerful Sensor Networks}}}, doi = {{10.1109/TNET.2012.2227792}}, volume = {{21}}, 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{37006, abstract = {{In this paper we present an approach for the configuration and reconfiguration of FlexRay networks to increase their fault tolerance. To guarantee a correct and deterministic system behavior, the FlexRay specification does not allow a reconfiguration of the schedapproachule during run time. To avoid the necessity of a complete bus restart in case of a node failure, we propose a reconfiguration using redundant slots in the schedule and/or combine messages in existing frames and slots, to compensate node failures and increase robustness. Our approach supports the developer to increase the fault tolerance of the system during the design phase. It is a heuristic, which, additionally to a determined initial configuration, calculates possible reconfigurations for the remaining nodes of the FlexRay network in case of a node failure, to keep the system working properly. An evaluation by means of realistic safety-critical automotive real-time systems revealed that it determines valid reconfigurations for up to 80% of possible individual node failures. In summary, our approach offers major support for the developer of FlexRay networks since the results provide helpful feedback about reconfiguration capabilities. In an iterative design process these information can be used to determine and optimize valid reconfigurations.}}, author = {{Klobedanz, Kay and König, Andreas and Müller, Wolfgang}}, booktitle = {{Proceedings of DATE'11}}, keywords = {{Schedules, Fault tolerant systems, Redundancy, Protocols, Automotive engineering, Genetic algorithms}}, location = {{Grenoble, France}}, publisher = {{IEEE}}, title = {{{A Reconfiguration Approach for Faul-Tolerant FlexRay Networks}}}, doi = {{10.1109/DATE.2011.5763022}}, year = {{2011}}, } @inproceedings{5685, abstract = {{In double-sided markets for computing resources an optimal allocation schedule among job offers and requests subject to relevant capacity constraints can be determined. With increasing storage demands and emerging storage services the question how to schedule storage jobs becomes more and more interesting. Since such scheduling problems are often in the class NP-complete an exact computation is not feasible in practice. On the other hand an approximation to the optimal solution can easily be found by means of using heuristics. The problem with this attempt is that the suggested solution may not be exactly optimal and is thus less satisfying. Considering the two above mentioned solution approaches one can clearly find a trade-off between the optimality of the solution and the efficiency to get to a solution at all. This work proposes to apply and combine heuristics in optimization to gain from both of their benefits while reducing the problematic aspects. Following this method it is assumed to get closer to the optimal solution in a shorter time compared to a full optimization.}}, author = {{Finkbeiner, Josef and Bodenstein, Christian and Schryen, Guido and Neumann, Dirk}}, booktitle = {{18th European Conference on Information Systems (ECIS 2010)}}, keywords = {{Decision Support System, Algorithms, Optimization, Market Engineering}}, title = {{{Applying heuristic methods for job scheduling in storage markets}}}, year = {{2010}}, } @article{64055, abstract = {{A program for iterative fitting procedures to determine the NMR parameters from 51V solid-state MAS NMR spectra was developed. It contains options to use genetic algorithms and downhill-simplex optimizing procedures to extract the optimal parameter sets, which describe our spectra. As computational kernel the SIMPSON program is employed. Other kernels like SPINEVOLUTION are easily incorporable. The algorithms are checked for their suitability for the present optimization problem and optimal simulation conditions are determined, with the focus on minimal processing time. The procedure leads to a very good agreement between experimental and simulated spectra in a passable period of time. First results for spectra of model compounds for the active site of vanadium haloperoxidases are presented.}}, author = {{Waechtler, Maria and Schweitzer, Annika and Gutmann, Torsten and Breitzke, Hergen and Buntkowsky, Gerd}}, journal = {{Solid State Nuclear Magnetic Resonance}}, keywords = {{51V MAS NMR spectroscopy, Genetic algorithms, Iterative fitting procedures, Model complexes for vanadium haloperoxidases}}, number = {{1}}, pages = {{37–48}}, title = {{{Efficient analysis of 51V solid-state MAS NMR spectra using genetic algorithms}}}, doi = {{10.1016/j.ssnmr.2008.11.003}}, volume = {{35}}, year = {{2009}}, } @inproceedings{6508, abstract = {{In this paper, we present a framework that supports experimenting with evolutionary hardware design. We describe the framework's modules for composing evolutionary optimizers and for setting up, controlling, and analyzing experiments. Two case studies demonstrate the usefulness of the framework: evolution of hash functions and evolution based on pre-engineered circuits.}}, author = {{Kaufmann, Paul and Platzner, Marco}}, booktitle = {{Second NASA/ESA Conference on Adaptive Hardware and Systems (AHS 2007)}}, isbn = {{076952866X}}, keywords = {{integrated circuit design, hardware evolution, evolutionary hardware design, evolutionary optimizers, hash functions, preengineered circuits, Hardware, Circuits, Design optimization, Visualization, Genetic programming, Genetic mutations, Clustering algorithms, Biological cells, Field programmable gate arrays, Routing}}, location = {{Edinburgh, UK}}, pages = {{447--454}}, publisher = {{IEEE}}, title = {{{MOVES: A Modular Framework for Hardware Evolution}}}, doi = {{10.1109/ahs.2007.73}}, year = {{2007}}, } @inproceedings{11930, abstract = {{For human-machine interfaces in distant-talking environments multichannel signal processing is often employed to obtain an enhanced signal for subsequent processing. In this paper we propose a novel adaptation algorithm for a filter-and-sum beamformer to adjust the coefficients of FIR filters to changing acoustic room impulses, e.g. due to speaker movement. A deterministic and a stochastic gradient ascent algorithm are derived from a constrained optimization problem, which iteratively estimates the eigenvector corresponding to the largest eigenvalue of the cross power spectral density of the microphone signals. The method does not require an explicit estimation of the speaker location. The experimental results show fast adaptation and excellent robustness of the proposed algorithm.}}, author = {{Warsitz, Ernst and Haeb-Umbach, Reinhold}}, booktitle = {{IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2005)}}, keywords = {{acoustic filter-and-sum beamforming, acoustic room impulses, acoustic signal processing, adaptive principal component analysis, adaptive signal processing, architectural acoustics, constrained optimization problem, cross power spectral density, deterministic algorithm, deterministic algorithms, distant-talking environments, eigenvalues and eigenfunctions, eigenvector, enhanced signal, filter-and-sum beamformer, FIR filter coefficients, FIR filter coefficients, FIR filters, gradient methods, human-machine interfaces, iterative estimation, iterative methods, largest eigenvalue, microphone signals, multichannel signal processing, optimisation, principal component analysis, spectral analysis, stochastic gradient ascent algorithm, stochastic processes}}, pages = {{iv/797--iv/800 Vol. 4}}, title = {{{Acoustic filter-and-sum beamforming by adaptive principal component analysis}}}, doi = {{10.1109/ICASSP.2005.1416129}}, volume = {{4}}, year = {{2005}}, }