Abstract

Evolutionary algorithms possess several characteristics that are desirable for problems involving i) multiple conflicting objectives, and ii) intractably large and highly complex search spaces. This is typical for many optimization problems in the field of computer engineering. Consider the design of a computer system. An optimal design might be an architecture that minimizes cost while minimizing the overall power consumption. However, these goals are generally conflicting: low-power architectures substantially increase cost, while cheap architectures usually need a lot of power - none of these solutions can be said to be superior without further consideration. As a consequence, there is no single optimum, but rather a set of alternative optima, generally known as Pareto-optimal solutions. Evolutionary algorithms are able to capture multiple Pareto-optimal solutions in a single simulation run. Obtaining high convergence and diversity of solutions in low computational time are the important issues in multi-objective optimization using evolutionary algorithms.

Examples of our current research works are given below.

MOPSO - Different from evolutionary computation techniques, Particle Swarm Optimization (PSO) method is motivated from the simulation of social behavior of bird flocking and fish schooling. PSO was originally designed and developed by Eberhart and Kennedy. However, it shares many similarities with evolutionary computation techniques. The system is initialized with a population of random solutions and searches for optima by updating generations. Unlike EA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions fly through the problem space by following the current optimum.  In PSO, each single solution is a "bird" in the search space. We call it "particle". All of particles have fitness values which are evaluated by the fitness function to be optimized and have velocities which direct the flying of the particles. The particles fly through the problem space by following the current optimum particle called guide.

• Covering approximated Pareto-optimal fronts
• Diversity metric- Sigma diversity metric
• Hybrid MOEA: Hybrid MOEA (HMOEA) is a combination of MOEA with Subdivision method to obtain controllable exploration of the search space. Here, we cover the approximated Pareto-optimal front of some multi-objective problems.

Hierarchical Chromosomes in System Synthesis -  We propose an approach for solving hierarchical multi-objective optimization problems (MOPs). In realistic MOPs, two main challenges have to be considered: (i) the complexity of the search space and (ii) the non-monotonicity of the objective-space. Here, we introduce a hierarchical problem description (Chromosomes) to deal with the complexity of the search space. Since evolutionary algorithms have  been proved to provide good solutions in non-monotonic objective-spaces, we apply genetic operators also on the structure of hierarchical Chromosomes This novel approach decreases exploration time substantially.

Visual Programming of Evolutionary Algorithms - While an evolutionary algorithm is a powerful optimization concept, one of its drawbacks is the difficulty of implementing it. Users would require some programming expertise to write a computer program that implement their algorithm according to their need. This has to be done before they can carry out their design task, where they should be really engaged in. A simple solution to this problem has been addressed in our research group. In the right Figure a graphical user interface is shown, where a user can construct his particular evolutionary algorithm graphically, including entering the fitness function, chromosome structure, choosing operators for selection and recombination as well as drawing the flowchart of the complete evolutionary algorithm. Even hybrid combinations of different evolutionary algorithm methods such as Genetic Algorithms operating on bitstrings and Genetic Programming using tree-like Chromosomes can be achieved. For the graphical input, java code will be emitted and the ready evolutionary algorithm is compiled and can be run directly. The program has a comprehensive user interface and also powerful graphical displays for ease-of-use and visualization of simulation results.

Studienarbeiten and Diploma Theses

2012

20 J. Falk, C. Zebelein, C. Haubelt and J. Teich. A Rule-Based Quasi-Static Scheduling Approach for Static Islands in Dynamic Dataflow Graphs. ACM Transactions on Embedded Computing Systems (to appear). ©1

19 M. Eberl, M. Glaß, J. Teich and U. Abelein. Considering Diagnosis Functionality during Automatic System-Level Design of Automotive Networks. To appear in Proceedings of the 49th Design Automation Conference (DAC 2012), San Francisco, USA, Jun. 3-7, 2012. ©1

18 J. Teich. Hardware/Software Co-Design: Past, Present, and Predicting the Future. To appear in Proceedings of the IEEE, vol. 100, no. 5, May 2012. Invited paper. [DOI: 10.1109/JPROC.2011.2182009]. ©1

17 R. Membarth, F. Hannig, J. Teich, M. Körner and W. Eckert. Generating Device-specific GPU Code for Local Operators in Medical Imaging. To appear in Proceedings of the 26th IEEE International Parallel & Distributed Processing Symposium (IPDPS), Shanghai, China, May 21-25, 2012. ©3

16 S. Graf, T. Russ, M. Glaß and J. Teich. Considering MOST150 during Virtual Prototyping of Automotive E/E Architectures. To appear in Proceedings of Automotive meets Electronics (AmE 2012), Dortmund, Germany, April 17-18, 2012. ©1

15 S. Helwig, J. Branke and S. Mostaghim. Experimental Analysis of Bound Handling Techniques in Particle Swarm Optimization. IEEE Transactions on Evolutionary Computation, 2011, to appear. ©1

14 Y. Xu, B. Li, R. Hasholzner, B. Rohfleisch, C. Haubelt and J. Teich. Variation-Aware Leakage Power Model Extraction for System-Level Hierarchical Power Analysis. To appear in Proceedings of the Design, Automation and Test in Europe Conference (DATE), March 2012. ©1

13 P. Milbredt, M. Glaß, M. Lukasiewycz, A. Steininger and J. Teich. Designing FlexRay-based Automotive Architectures: A Holistic OEM Approach. To appear in Proceedings of Design, Automation and Test in Europe (DATE 2012), Dresden, Germany, March 12-16, 2012. ©1

12 C. Zebelein, J. Falk, C. Haubelt and J. Teich. Exploiting Model-Knowledge in High-Level Synthesis. In Workshop für Methoden und Beschreibungssprachen zur Modellierung und Verifikation von Schaltungen und Systemen (MBMV’12), in Druck, Kaiserslautern, Mar. 5-7, 2012. ©1

11 S. Graf, M. Glaß and J. Teich. Unreliable Data Transmissions and Limited Hardware Communication Buffers in Automotive E/E Virtual Prototypes. To appear in Proceedings of Methoden und Beschreibungssprachen zur Modellierung und Verifikation von Schaltungen und Systemen (MBMV 2012), Kaiserslautern, Germany, March 05-07, 2012. ©1

10 L. Zhang, M. Glaß, M. Streubühr, J. Teich, A. v. Schwerin and K. Liu. Actor-oriented Modeling and Simulation of Cut-through Communication in Network Controllers. To appear in Proceedings of Methoden und Beschreibungssprachen zur Modellierung und Verifikation von Schaltungen und Systemen (MBMV 2012), Kaiserslautern, Germany, March 05-07, 2012. ©1

9 Y. Xu, R. Rosales, B. Wang, M. Streubühr, R. Hasholzner, C. Haubelt and J. Teich. A Very Fast and Quasi-Accurate Power-State-Based System-Level Power Modeling Methodology. To appear In Proceedings of the International Conference on Architecture of Computing Systems (ARCS), Munich, Germany, March 2012. ©1

8 L. Zhang, M. Streubühr, M. Glaß and J. Teich. System-Level Modeling and Simulation of Networked PROFINET IO Controllers. To appear in Proceedings of the Embedded World Conference, Nuremberg, Germany, February 28 - March 01, 2012. ©1

7 D. Koch, J. Torresen, C. Beckhoff, D. Ziener, C. Dennl, V. Breuer, J. Teich, M. Feilen and W. Stechele. Partial Reconfiguration on FPGAs in Practice - Tools and Applications. To appear in Tutorial Proceedings of the 2012 Architecture of Computing Systems (ARCS'12), Munich, Germany. February 28-29, 2012. ©1

6 C. Rieß, V. Strehl and R. Wanka. The Spectral Relation between the Cube-Connected Cycles and the Shuffle-Exchange Network. in: Proc. 10th Workshop on Parallel Systems and Algorithms (PASA) of the 25th Int. Conf. on Architecture of Computing Systems (ARCS); 2012, to appear. ©1

5 R. Membarth, J. Lupp, F. Hannig, J. Teich, M. Körner and W. Eckert. Dynamic Task-Scheduling and Resource Management for GPU Accelerators in Medical Imaging. To appear in Proceedings of the 24th International Conference on Architecture of Computing Systems (ARCS), Munich, Germany, February 28 - March 02, 2012. ©1

4 S. Wildermann, J. Angermeier, E. Sibirko and J. Teich. Placing Multi-mode Streaming Applications on Dynamically Partially Reconfigurable Architectures. International Journal of Reconfigurable Computing. Volume 2012 (2012), Article ID 608312, 12 pages. [doi:10.1155/2012/608312] . ©1

3 J. Henkel, A. Herkersdorf, L. Bauer, T. Wild, M. Hübner, R. Kumar Pujari, A. Grudnitsky, J. Heisswolf, A. Zaib, B. Vogel, V. Lari and S. Kobbe. Invasive Manycore Architectures. Proceedings of the 17th Asia and South Pacific Design Automation Conference (ASP-DAC), pp. 193-200, Sydney, Australia, Jan. 30- Feb. 2, 2012. ©3

2 S. Roloff, F. Hannig and J. Teich. Approximate Time Functional Simulation of Resource-Aware Programming Concepts for Heterogeneous MPSoCs. Proceedings of the 17th Asia and South Pacific Design Automation Conference (ASP-DAC), pp. 187-192, Sydney, Australia, January 30-February 2, 2012. ©1

1 S. Fekete, T. Kamphans, N. Schweer, C. Tessars, J. van der Veen, J. Angermeier, D. Koch and J. Teich. Dynamic Defragmentation of Reconfigurable Devices. ACM Transactions on Reconfigurable Technology and Systems (TRETS), 2012. ©1