2001

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• Nicole Drechsler, Frank Schmiedle, Daniel Große, Rolf Drechsler
  Heuristic Learning based on Genetic Programming
  2001 EuroGP, Springer Verlag, volume: 2038, pages: 1 - 10
• Frank Schmiedle, Nicole Drechsler, Daniel Große, Rolf Drechsler
  Priorities in Multi-Objective Optimization for Genetic Programming
  2001 Conf. on Genetic and Evolutionary Computation, pages: 129 - 136
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  Abstract

  A new technique for multi-objective optimization is presented that allows to include priorities. But in contrast to previous techniques they can be included very easily and do not require much user interaction. The new approach is studied from a theoretical and practical point of view. The main differences to existing methods, like relation dominate and favor, are discussed. An experimental study of applying priorities in heuristics learning based on Genetic Programming (GP) is described. The experiments confirm the advantages presented in comparison to several other techniques.

2000

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• Rolf Drechsler, Nicole Drechsler, Elke Mackensen, Tobias Schubert, Bernd Becker
  Design Reuse by Modularity: A Scalable Dynamical (Re)Configurable Multiprocessor System
  2000 Euromicro Conf., pages: 1:425 - 431
• Rolf Drechsler, Nicole Drechsler, Wolfgang Günther
  Fast Exact Minimization of BDDs
  2000 IEEE Trans. on CAD, volume: 19, issue: 3, pages: 384 - 389
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  Abstract

  We present a new exact algorithm for finding the optimal variable ordering for reduced ordered Binary Decision Diagrams (BDDs). The algorithm makes use of a lower bound technique known from VLSI design. Up to now this technique has been used only for theoretical considerations and it is adapted here for our purpose. Furthermore, the algorithm supports symmetry aspects and uses a hashing based data structure. Experimental results are given to demonstrate the efficiency of our approach. We succeeded in minimizing several functions, including adders with up to 64 variables, for which all other previously presented approaches fail.
• Tobias Schubert, Elke Mackensen, Nicole Drechsler, Rolf Drechsler, Bernd Becker
  Specialized Hardware for Implementation of Evolutionary Algorithms
  2000 Int'l Workshop on Boolean Problems, pages: 175 - 182
• Tobias Schubert, Elke Mackensen, Nicole Drechsler, Rolf Drechsler, Bernd Becker
  Specialized Hardware for Implementation of Evolutionary Algorithms
  2000 Conf. on Genetic and Evolutionary Computation, page: 369
• Wolfgang Günther, Nicole Drechsler, Rolf Drechsler, Bernd Becker
  Verification of Designs Containing Black Boxes
  2000 GI/ITG/GMM Workshop “Methoden und Beschreibungssprachen zur Modellierung und Verifikation von Schaltungen und Systemen”
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  Abstract

  Often modern designs contain regions where the implementation of certain components is not (fully) known. These regions are called black boxes in the following. They occur e.g. if different designers work on a project in parallel or if IP cores are used. In this paper an approach based on a symbolic representation of characteristic functions for verifying circuits with black boxes is presented. We show that by this method more faults can be detected than with pure binary simulation and symbolic simulation using BDDs, respectively, only. This results from the formulation of our algorithm that allows implications over the black box. Experimental results are given to show what parts of a design can be proven to be correct, if black boxes are assumed. Of course, the probability for the detection of a fault in general depends on the size of the unknown regions. But fault injection experiments on benchmarks show that for many circuits even up to 90 percent of the faults are detected, even though large parts of the design are unspecified.
• Wolfgang Günther, Nicole Drechsler, Rolf Drechsler, Bernd Becker
  Verification of Designs Containing Black Boxes
  2000 Euromicro Conf., pages: 100 - 105
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  Abstract

  Often modern designs contain regions where the implementation of certain components is not (fully) known. These regions are called black boxes in the following. They occur e.g. if different designers work on a project in parallel or if IP cores are used. In this paper an approach based on a symbolic representation of characteristic functions for verifying circuits with black boxes is presented. We show that by this method more faults can be detected than with pure binary simulation and symbolic simulation using BDDs, respectively, only. This results from the formulation of our algorithm that allows implications over the black box. Experimental results are given to show what parts of a design can be proven to be correct, if black boxes are assumed. Of course, the probability for the detection of a fault in general depends on the size of the unknown regions. But fault injection experiments on benchmarks show that for many circuits even up to 90 percent of the faults are detected, even though large parts of the design are unspecified.

1999

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• Nicole Drechsler, Rolf Drechsler, Bernd Becker
  A New Model for Multi-Objective Optimization in Evolutionary Algorithms
  1999 Int'l Conf. on Computational Intelligence, Springer Verlag, volume: 1625, pages: 108 - 117
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  Abstract

  Many optimization problems consist of several mutually dependent subproblems, where the resulting solutions must satisfy all requirements. We propose a new model for Multi-Objective Optimization (MOO) in Evolutionary Algorithms (EAs). The search space is partitioned into so-called Satisfiability Classes (SC), where each region represents the quality of the optimization criteria. Applying the SCs to individuals in a population a fitness can be assigned during the EA run. The model also allows the handling of infeasible regions and restrictions in the search space. Additionally, different priorities for optimization objectives can be modeled. Advantages of the model over previous approaches are discussed and an application is given that shows the superiority of the method for modeling MOO problems.
• Martin Keim, Nicole Drechsler, Bernd Becker
  Combining GAs and Symbolic Methods for High Quality Tests of Sequential Circuits
  1999 ASP Design Automation Conf., pages: 315 - 318
• Nicole Drechsler, Rolf Drechsler
  Exploiting Don't Cares During Data Sequencing using Genetic Algorithms
  1999 ASP Design Automation Conf., pages: 303 - 306
• Rolf Drechsler, Nicole Drechsler
  VLSI CAD and the Integration of Evolutionary Techniques
  CRC Press International Series on Computational Intelligence, 1999

1998

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• Martin Keim, Nicole Drechsler, Rolf Drechsler, Bernd Becker
  Combining GAs and Symbolic Methods for High Quality Tests of Sequential Circuits
  1998 European Test Workshop, pages: 141 - 142
• Martin Keim, Nicole Drechsler, Rolf Drechsler, Bernd Becker
  Test Generation for (Sequential) Multi-Valued Logic Networks based on Genetic Algorithm
  1998 Int'l Symp. on Multi-Valued Logic, pages: 215 - 220