Solving hierarchical optimization problems using MOEAs

Teich J, Haubelt C, Mostaghim S, Tyagi A (2003)

Publication Status: Published

Publication Type: Book chapter / Article in edited volumes

Publication year: 2003

Publisher: Springer

Edited Volumes: Evolutionary Multi-Criterion Optimization

Series: Lecture Notes in Computer Science

City/Town: Berlin, Heidelberg, New York

Book Volume: 2632

Pages Range: 162-176

URI: https://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=35248891516&origin=inward

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. © Springer-Verlag Berlin Heidelberg 2003.

Authors with CRIS profile

Jürgen Teich Lehrstuhl für Informatik 12 (Hardware-Software-Co-Design) Christian Haubelt Technische Fakultät

Related research project(s)

SystemCoDesigner: System level design space exploration for embedded systems (SystemCoDesigner) April 1, 2005 - April 1, 2008 EVOlutionary Learning by Intelligent Variation and choice of suitable Operator sets (EVOLIVO) Oct. 1, 1998 - March 31, 2005

Involved external institutions

Universität Paderborn DE Germany (DE)

How to cite

APA:

Teich, J., Haubelt, C., Mostaghim, S., & Tyagi, A. (2003). Solving hierarchical optimization problems using MOEAs. In Carlos M. Fonseca, Peter J. Fleming, Eckart Zitzler, Kalyanmoy Deb, and Lothar Thiele (Eds.), Evolutionary Multi-Criterion Optimization. (pp. 162-176). Berlin, Heidelberg, New York: Springer.

MLA:

Teich, Jürgen, et al. "Solving hierarchical optimization problems using MOEAs." Evolutionary Multi-Criterion Optimization. Ed. Carlos M. Fonseca, Peter J. Fleming, Eckart Zitzler, Kalyanmoy Deb, and Lothar Thiele, Berlin, Heidelberg, New York: Springer, 2003. 162-176.

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