Network Optimization for Genomic Data Analysis and Wireless System Design

Background

Mathematical modeling and optimization is often carried out in an iterative process where both model and algorithm are progressively refined until a satisfactory solution has been found. Such refinements are based on preference information and domain knowledge which may vary for each user and application. Therefore, optimization methods need to be flexible in this respect and at the same time allow model and preference modifications during run-time; however, current techniques are not designed for flexible and interactive preference articulation.

Goals

The anticipated results of this project can be summarized as follows: (i) a methodology for formalizing user preferences such that the resulting model can be easily used for optimization; (ii) randomized search algorithms that allow to integrate arbitrary user preferences and guide the optimization process with regard to these preferences; (iii) hybridization schemes that allow to integrate complementary operations research techniques and problem-specific strategies such as branch-and-bound and dynamic programming into global search; (iv) concepts for dynamic preference adaptation.

The project addresses two application domains, namely the study of gene regulation in living cells and the design of embedded systems based on wireless communication, and integrates them in an interdisciplinary approach by focusing on network optimization problems that arise in both areas and are characterized by similar features.

Collaborators

• L. Thiele, Computer Engineering and Networks Laboratory, ETH Zurich, Switzerland
• K. Deb and D. Kumar Saxena, Kanpur Genetic Algorithms Laboratory, Indian Institute of Technology, Kanpur, India

Funding

• Indo Swiss Joint Research Programme (ISJRP)

Publications

[1 — bade2010a]

J. Bader. Hypervolume-Based Search for Multiobjective Optimization: Theory and Methods. PhD thesis, ETH Zurich, Switzerland, 2010. (PDF) (bibtex) (online access)

[2 — bz2010a]

J. Bader and E. Zitzler. Robustness in Hypervolume-based Multiobjective Search. TIK Report 317, Computer Engineering and Networks Laboratory (TIK), ETH Zurich, 2010. (PDF) (bibtex)

[3 — wbhb2010a]

M. Woehrle, D. Brockhoff, T. Hohm, and S. Bleuler. Investigating Coverage and Connectivity Trade-offs in Wireless Sensor Networks: The Benefits of MOEAs. In M. Ehrgott et al., editors, Multiple Criteria Decision Making for Sustainable Energy and Transportation Systems (MCDM 2008), volume 634 of LNEMS, pages 211–221, Heidelberg, Germany, 2010. Springer. (bibtex)

[4 — bdz2008a]

J. Bader, K. Deb, and E. Zitzler. Faster Hypervolume-based Search using Monte Carlo Sampling. In M. Ehrgott et al., editors, Conference on Multiple Criteria Decision Making (MCDM 2008), volume 634 of LNEMS, pages 313–326, Heidelberg, Germany, 2010. (bibtex)

[5 — abbz2009b]

A. Auger, J. Bader, D. Brockhoff, and E. Zitzler. Articulating User Preferences in Many-Objective Problems by Sampling the Weighted Hypervolume. In G. Raidl et al., editors, Genetic and Evolutionary Computation Conference (GECCO 2009), pages 555–562, New York, NY, USA, 2009. ACM. (PDF) (bibtex)

[6 — abbz2009c]

A. Auger, J. Bader, D. Brockhoff, and E. Zitzler. Investigating and Exploiting the Bias of the Weighted Hypervolume to Articulate User Preferences. In G. Raidl et al., editors, Genetic and Evolutionary Computation Conference (GECCO 2009), pages 563–570, New York, NY, USA, 2009. ACM. (PDF) (bibtex)

[7 — bz2009d]

J. Bader and E. Zitzler. HypE: An Algorithm for Fast Hypervolume-Based Many-Objective Optimization. Evolutionary Computation, page no appear, 2009. to appear. (bibtex)

[8 — bbwz2009a]

J. Bader, D. Brockhoff, S. Welten, and E. Zitzler. On Using Populations of Sets in Multiobjective Optimization. In M. Ehrgott et al., editors, Conference on Evolutionary Multi-Criterion Optimization (EMO 2009), volume 5467 of LNCS, pages 140–154. Springer, 2009. (PDF) (bibtex)

[9 — bz2009b]

J. Bader and E. Zitzler. A Hypervolume-Based Optimizer for High-Dimensional Objective Spaces. In Conference on Multiple Objective and Goal Programming (MOPGP 2008), Lecture Notes in Economics and Mathematical Systems. Springer, 2009. (PDF) (bibtex) (suppl. material)

[10 — ztb2008c]

E. Zitzler, L. Thiele, and J. Bader. On Set-Based Multiobjective Optimization (Revised Version). TIK Report 300, Computer Engineering and Networks Laboratory (TIK), ETH Zurich, December 2008. (PDF) (bibtex)

[11 — bz2008a]

J. Bader and E. Zitzler. HypE: An Algorithm for Fast Hypervolume-Based Many-Objective Optimization. TIK Report 286, Computer Engineering and Networks Laboratory (TIK), ETH Zurich, November 2008. (PDF) (bibtex) (suppl. material)

[12 — ztb2008d]

E. Zitzler, L. Thiele, and J. Bader. On Set-Based Multiobjective Optimization. IEEE Transactions on Evolutionary Computation, 2009. to appear. (bibtex)

[13 — wbhb2008b]

M. Woehrle, D. Brockhoff, T. Hohm, and S. Bleuler. Investigating Coverage and Connectivity Trade-offs in Wireless Sensor Networks: The Benefits of MOEAs. TIK Report 294, Computer Engineering and Networks Laboratory (TIK), ETH Zurich, October 2008. (PDF) (bibtex)

[14 — ztb2008b]

E. Zitzler, L. Thiele, and J. Bader. SPAM: Set Preference Algorithm for Multiobjective Optimization. In G. Rudolph et al., editors, Conference on Parallel Problem Solving From Nature (PPSN X), volume 5199 of LNCS, pages 847–858. Springer, 2008. (PDF) (bibtex) (online access)

[15 — bzfz2008a]

S. Bleuler, P. Zimmermann, M. Friberg, and E. Zitzler. Discovering Trends in Gene Expression Data Using a Hybrid Evolutionary Algorithm. Algorithmic Operations Research, 3(2), 2008. (bibtex)

[16 — bbz2007a]

S. Bleuler, J. Bader, and E. Zitzler. Reducing Bloat in GP with Multiple Objectives. In J. Knowles, D. Corne, and K. Deb, editors, Multiobjective Problem Solving from Nature: From Concepts to Applications, pages 177–200. Springer, 2007. (bibtex) (online access)

[17 — wbh2007a]

M. Woehrle, D. Brockhoff, and T. Hohm. A New Model for Deployment Coverage and Connectivity of Wireless Sensor Networks. TIK-Report 278, Computer Engineering and Networks Laboratory (TIK), ETH Zurich, September 2007. (PDF) (bibtex)

[18 — ktz2006a]

J. Knowles, L. Thiele, and E. Zitzler. A Tutorial on the Performance Assessment of Stochastic Multiobjective Optimizers. TIK Report 214, Computer Engineering and Networks Laboratory (TIK), ETH Zurich, February 2006. (PDF) (bibtex)