Abstract
In this paper, 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 proven 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. The example of system synthesis is used as a case study to illustrate the necessity and the benefits of hierarchical optimization.
This work was supported in part by the German Science Foundation (DFG), SPP 1040.
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References
Josephson, J.R., Chandrasekaran, B., Carroll, M., Iyer, N., Wasacz, B., Rizzoni, G., Li, Q., Erb, D.A.: An Architecture for Exploring Large Design Spaces. In: Proc. of the Nat. Conference of AI (AAAI-98), Madison, Wisconsin (1998) 143–150
Abraham, S.G., Rau, B.R., Schreiber, R.: Fast Design Space Exploration Through Validity and Quality Filtering of Subsystem Designs. Technical report, Hewlett Packard, Compiler and Architecture Research, HP Laboratories Palo Alto (2000)
Rudolph, G., Agapie, A.: Convergence Properties of Some Multi-Objective Evolutionary Algorithms. In: Proc. of the 2000 Congress on Evolutionary Computation, Piscataway, NJ, IEEE Service Center (2000) 1010–1016
Dasgupta, D., McGregor, D.R.: Nonstationary Function Optimization using the Structured Genetic Algorithm. In Männer, R., Manderick, B., eds.: Proceedings of Parallel Problem Solving from Nature (PPSN 2), Brussels, Belgium, Elsevier Science (1992) 145–154
Blickle, T., Teich, J., Thiele, L.: System-Level Synthesis Using Evolutionary Algorithms. In Gupta, R., ed.: Design Automation for Embedded Systems. 3. Kluwer Academic Publishers, Boston (1998) 23–62
Dick, R., Jha, N.: MOGAC: A Multiobjective Genetic Algorithm for Hardware-Software Cosynthesis of Distributed Embedded Systems. In: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 17(10). (1998) 920–935
Pareto, V.: Cours d’Économie Politique. Volume 1. F. Rouge & Cie., Lausanne, Switzerland (1896)
Thiele, L., Chakraborty, S., Gries, M., Künzli, S.: A Framework for Evaluating Design Tradeoffs in Packet Processing Architectures. In: Proceedings of the 39th Design Automation Conference (DAC 2002). (2002) 880–885
Teich, J., Haubelt, C., Mostaghim, S., Slomka, F., Tyagi, A.: Techniques for Hierarchical Design Space Exploration and their Application on System Synthesis. Technical Report 1/2002, Institute Date, Department of EE and IT, University of Paderborn, Paderborn, Germany (2002)
Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the Strength Pareto Evolutionary Algorithm. Technical report, Swiss Federal Institute of Technology (ETH) Zurich (2001) TIK-Report 103. Department of Electrical Engineering.
Haubelt, C., Teich, J., Richter, K., Ernst, R.: Flexibility/Cost-Tradeoffs in Platform-Based Design. In Deprettere, E., Teich, J., Vassiliadis, S., eds.: Embedded Processor Design Challenges. Volume 2268 of Lecture Notes in Computer Science (LNCS)., Berlin, Heidelberg, Springer (2002) 38–56
Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons (2001)
Zitzler, E.: Evolutionary Algorithms for Multiobjective Optimization: Methods and Applications. PhD thesis, Department of Electrical Engineering, Swiss Federal Institute of Technology (ETH) Zurich (1999)
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Haubelt, C., Mostaghim, S., Teich, J., Tyagi, A. (2003). Solving Hierarchical Optimization Problems Using MOEAs. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Thiele, L., Deb, K. (eds) Evolutionary Multi-Criterion Optimization. EMO 2003. Lecture Notes in Computer Science, vol 2632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36970-8_12
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DOI: https://doi.org/10.1007/3-540-36970-8_12
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