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Multi-objective Optimisation Based on Relation Favour

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1993))

Abstract

Many optimisation problems in circuit design, in the following also refereed to as VLSI CAD, consist of mutually dependent sub-problems, where the resulting solutions must satisfy several requirements. Recently, a new model for Multi-Objective Optimisation (MOO) for applications in Evolutionary Algorithms (EAs) has been proposed. The search space is partitioned into so-called Satisfiability Classes (SCs), where each region represents the quality of the optimisation 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 optimisation objectives can be modelled. In this paper, the model is studied in further detail. Various properties are shown and advantages and disadvantages are discussed. The relations to other techniques are presented and experimental results are given to demonstrate the efficiency of the model.

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References

  1. R.E. Bryant. Graph-based algorithms for Boolean function manipulation. IEEE Trans. on Comp., 35(8):677–691, 1986.

    Article  MATH  Google Scholar 

  2. T.H. Cormen, C.E. Leierson, and R.C. Rivest. Introduction to Algorithms. MIT Press, McGraw-Hill Book Company, 1990.

    Google Scholar 

  3. N. Drechsler. Über die Anwendung Evolutionärer Algorithmen in Schaltkreisentwurf, PhD thesis, University of Freiburg, Germany, 2000.

    Google Scholar 

  4. R. Drechsler. Evolutionary Algorithms for VLSI CAD. Kluwer Academic Publisher, 1998.

    Google Scholar 

  5. R. Drechsler and B. Becker. Learning heuristics by genetic algorithms. In ASP Design Automation Conf., pages 349–352, 1995.

    Google Scholar 

  6. N. Drechsler, R. Drechsler, and B. Becker. Multi-objective optimization in evolutionary algorithms using satisfiability classes. In International Conference of Computational Intelligence, 6th Fuzzy Days, LNCS 1625, pages 108–117, 1999.

    Google Scholar 

  7. R. Drechsler, N. Göckel, and B. Becker. Learning heuristics for OBDD minimization by evolutionary algorithms. In Parallel Problem Solving from Nature, LNCS 1141, pages 730–739, 1996.

    Chapter  Google Scholar 

  8. H. Esbensen. Defining solution set quality. Technical report, UCB/ERL M96/1, University of Berkeley, 1996.

    Google Scholar 

  9. H. Esbensen and E.S. Kuh. EXPLORER: an interactive floorplaner for design space exploration. In European Design Automation Conf., pages 356–361, 1996.

    Google Scholar 

  10. C.M. Fonseca and P.J. Fleming. An overview of evolutionary algorithms in multiobjective optimization. Evolutionary Computation, 3(1):1–16, 1995.

    Google Scholar 

  11. D.E. Goldberg. Genetic Algorithms in Search, Optimization & Machine Learning. Addision-Wesley Publisher Company, Inc., 1989.

    Google Scholar 

  12. J. Horn, N. Nafpliotis, and D. Goldberg. A niched pareto genetic algorithm for multiobjective optimization. In Int’l Conference on Evolutionary Computation, 1994.

    Google Scholar 

  13. Z. Michalewicz. Genetic Algorithms + Data Structures = Evolution Programs. Springer-Verlag, 1994.

    Google Scholar 

  14. N. Srinivas and K. Deb. Multiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary Computation, 2(3):221–248, 1995.

    Article  Google Scholar 

  15. S. Yang. Logic synthesis and optimization benchmarks user guide. Technical Report 1/95, Microelectronic Center of North Carolina, Jan. 1991.

    Google Scholar 

  16. E. Zitzler and L. Thiele. Multiobjective evolutionary algorithms: A comparative case study and the strength pareto approach. IEEE Trans. on Evolutionary Computation, 3(4):257–271, 1999.

    Article  Google Scholar 

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© 2001 Springer-Verlag Berlin Heidelberg

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Drechsler, N., Drechsler, R., Becker, B. (2001). Multi-objective Optimisation Based on Relation Favour . In: Zitzler, E., Thiele, L., Deb, K., Coello Coello, C.A., Corne, D. (eds) Evolutionary Multi-Criterion Optimization. EMO 2001. Lecture Notes in Computer Science, vol 1993. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44719-9_11

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  • DOI: https://doi.org/10.1007/3-540-44719-9_11

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41745-3

  • Online ISBN: 978-3-540-44719-1

  • eBook Packages: Springer Book Archive

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