Improving EA-based design space exploration by utilizing symbolic feasibility tests
Pages 1945 - 1952
Abstract
This paper will propose a novel approach in combining Evolutionary Algorithms with symbolic techniques in order to improve the convergence of the algorithm in the presence of large search spaces containing only few feasible solutions. Such problems can be encountered in many real-world applications. Here, we will use the example of design space exploration of embedded systems to illustrate the benefits of our approach. The main idea is to integrate symbolic techniques into the Evolutionary Algorithm to guide the search towards the feasible region. We will present experimental results showing the advantages of our novel approach.
References
[1]
A. H. Aguirre, S. B. Rionda, C. A. C. Coello, G. L. Lizárraga, and E. M. Montes. Handling Constraints using Multiobjective Optimization Concepts. International Journal for Numerical Methods in Engineering, 59(15):1989--2017, Apr. 2004.
[2]
S. Bleuler, M. Laumanns, L. Thiele, and E. Zitzler. PISA - A Platform and Programming Language Independent Interface for Search Algorithms. In Proceedings of the Second International Conference on Evolutionary Multi-Criterion Optimization, Lecture Notes in Computer Science (LNCS), volume 2632, pages 494--508, Faro, Protugal, Apr. 2003.
[3]
T. Blickle, J. Teich, and L. Thiele. System-Level Synthesis Using Evolutionary Algorithms. In R. Gupta, editor, Design Automation for Embedded Systems, 3, pages 23--62. Kluwer Academic Publishers, Boston, Jan. 1998.
[4]
K. Deb. Multi-Objective Optimization using Evolutionary Algorithms. John Wiley & Sons, Ltd., Chichester, New York, Weinheim, Brisbane, Singapore, Toronto, 2001.
[5]
R. P. Dick and N. K. Jha. MOGAC: A Multiobjective Genetic Algorithm for Hardware-Software Co-Synthesis of Distributed Embedded Systems. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 17(10):920--935, Oct. 1998.
[6]
M. Eisenring, L. Thiele, and E. Zitzler. Conflicting Criteria in Embedded System Design. IEEE Design & Test of Computers, 17(2):51--59, June 2000.
[7]
C. Haubelt, S. Mostaghim, F. Slomka, J. Teich, and A. Tyagi. Hierarchical Synthesis of Embedded Systems Using Evolutionary Algorithms. In R. Drechsler and N. Drechsler, editors, Evolutionary Algorithms for Embedded System Design, Genetic Algorithms and Evolutionary Computation (GENA), pages 63--104. Kluwer Academic Publishers, Boston, Dordrecht, London, 2003.
[8]
C. Haubelt, J. Teich, R. Feldmann, and B. Monien. SAT-Based Techniques in System Design. In Proceedings of Design, Automation and Test in Europe, pages 1168--1169, Munich, Germany, Mar. 2003.
[9]
X. Hu, G. W. Greenwood, and J. G. D'Ambrosio. An Evolutionary Approach to Hardware/Software Partitioning. In Proceedings of Parallel Problem Solving from Nature, pages 900--909, Berlin, Germany, Sept. 1996.
[10]
V. Kianzad and S. S. Bhattacharyya. CHARMED: A Multi-Objective Co-Synthesis Framework for Multi-Mode Embedded Systems. In Proceedings of the 15th IEEE International Conference on Application-Specific Systems, Architectures and Processors (ASAP'04), pages 28--40, Galveston, U.S.A., Sept. 2004.
[11]
M. Laumanns. Analysis and Applications of Evolutionary Multiobjective Optimization Algorithms. PhD thesis, Eidgenössische Technische Hochschule Zürich, Aug. 2003.
[12]
M. Laumanns, L. Thiele, K. Deb, and E. Zitzler. Combining Convergence and Diversity in Evolutionary Multi-Objective Optimization. Evolutionary Computation, 10(3):263--282, 2002.
[13]
C. Scholl, R. Drechsler, and B. Becker. Functional Simulation Using Binary Decision Diagrams. In Proceedings of the 1997 IEEE/ACM Int. Conference on Computer-Aided Design, pages 8--12, San Jose, USA, Nov. 1997.
[14]
D. Whitley. Permutations. In Evolutionary Computation 1 -- Basic Algorithms and Operators, pages 139--150. Institute of Physics Publishing, Bristol and Philadelphia, 2000.
[15]
E. Zitzler. Evolutionary Algorithms for Multiobjective Optimization: Methods and Applications. PhD thesis, Eidgenössische Technische Hochschule Zürich, Nov. 1999.
[16]
E. Zitzler, M. Laumanns, and L. Thiele. SPEA2: Improving the Strength Pareto Evolutionary Algorithm for Multiobjective Optimization. In Evolutionary Methods for Design, Optimisation, and Control, pages 19--26, Barcelona, Spain, 2002.
[17]
E. Zitzler and L. Thiele. Multiobjective Optimization Using Evolutionary Algorithms -- A Comparative Case Study. In Proceedings of Parallel Problem Solving from Nature -- PPSN-V, pages 292--301, Amsterdam, The Netherlands, Sept. 1998.
[18]
E. Zitzler, L. Thiele, M. Laumanns, C. M. Fonseca, and V. Grunert da Fonseca. Performance Assessment of Multiobjective Optimizers: An Analysis and Review. IEEE Transactions on Evolutionary Computation, 7(2):117--132, Apr. 2003.
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Published: 25 June 2005
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