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Branke, Jürgen; Avigad, Gideon; Moshaiov, Amiram
Publisher: WBS, University of Warwick
Languages: English
Types: Book
Subjects: QA, HB
Many real-world optimization problems are subject to uncertainty. A possible goal is then to find a solution which is robust in the sense that it has the best worst-case performance over all possible scenarios. However, if the problem also involves mul- tiple objectives, which scenario is “best” or “worst” depends on the user’s weighting of the different criteria, which is generally difficult to specify before alternatives are known. Evolutionary multi-objective optimization avoids this problem by searching for the whole front of Pareto optimal solutions. This paper extends the concept of Pareto dominance to worst case optimization problems and demonstrates how evolu- tionary algorithms can be used for worst case optimization in a multi-objective setting.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Avigad, G., A. Moshaiov, and N. Brauner (2005). MOEA-based approach to delayed decisions for robust conceptual design. In Applications of Evolutionary Computing, Volume 3449 of LNCS, pp. 584 - 589. Springer.
    • Branke, J. (1998). Creating robust solutions by means of an evolutionary algorithm. In A. E. Eiben, T. Ba¨ck, M. Schoenauer, and H.-P. Schwefel (Eds.), Parallel Problem Solving from Nature, Volume 1498 of LNCS, pp. 119-128. Springer.
    • Branke, J. (2001a). Evolutionary Optimization in Dynamic Environments. Kluwer.
    • Branke, J. (2001b). Reducing the sampling variance when searching for robust solutions. In L. S. et al. (Ed.), Genetic and Evolutionary Computation Conference (GECCO'01), pp. 235-242. Morgan Kaufmann.
    • Branke, J., K. Deb, H. Dierolf, and M. Osswald (2004). Finding knees in multi-objective optimization. In Parallel Problem Solving from Nature, Number 3242 in LNCS, pp. 722-731. Springer.
    • Buchholz, P. and A. Thu¨ mmler (2005). Enhancing evolutionary algorithms with statistical selection procedures for simulation optimization. In M. E. Kuhl et al. (Eds.), Winter Simulation Conference, pp. 842-852. IEEE.
    • Coello Coello, C. A., D. A. V. Veldhuizen, and G. B. Lamont (2002). Evolutionary Algorithms for Solving MultiObjective Problems. Kluwer.
    • Das, I. (2000). Robustness optimization for constrained nonlinear programming problems. Engineering Optimization 32(5), 585-618.
    • Daum, D., K. Deb, and J. Branke (2007). Reliability-based optimization for multiple constraints with evolutionary algorithms. In Congress on Evolutionary Computation, pp. ?? IEEE.
    • Deb, K. (2001). Multi-Objective Optimization using Evolutionary Algorithms. Wiley.
    • Deb, K. and H. Gupta (2005). Searching for robust pareto-optimal solutions in multi-objective optimization. In Evolutionary Multi-Criterion Optimization, Volume 3410 of LNCS, pp. 150-164. Springer.
    • Deb, K., D. Padmanabhan, S. Gupta, and A. K. Mall (2007). Reliability-based multi-objective optimization using evolutionary algorithms. In S. Obayashi et al. (Eds.), Evolutionary Multi-Criterion Optimization, Volume 4403 of LNCS, pp. 66-80. Springer.
    • Elishakoff, I., R. T. Haftka, and J. Fang (1994). Structural design under bounded uncertainty-optimization with anti-optimization. Computers and Structures 53, 1401-1405.
    • Jin, Y. and B. Sendhoff (2003). Trade-off between performance and robustness: An evolutionary multiobjective approach. In Evolutionary Multi-criterion Optimization, LNCS 2632, pp. 237-251. Springer.
    • Li, M., A. Azarm, and V. Aute (2005). A multi-objective genetic algorithm for robust design optimization. In Genetic and Evolutionary Computation Conference, pp. 771-778. ACM.
    • Lim, D., Y.-S. Ong, Y. Jin, B. Sendhoff, and B.-S. Lee (2006). Inverse multi-objective robust evolutionary design. Genetic Programming and Evolvable Machines 7(4), 383-404.
    • Loughlin, D. H. and S. R. Ranjithan (1999). Chance-constrained genetic algorithms. In Genetic and Evolutionary Computation Conference, pp. 369-376. Morgan Kaufmann.
    • Lua, L., P. K. Kannan, B. Besharati, and S. Azarm (2005). Design of robust new products under variability: marketing meets design. Journal of Product Innovation Management 22, 177-192.
    • Ong, Y.-S., P. B. Nair, and K. Y. Lum (2006). Max-min surrogate-assisted evolutionary algorithm for robust design. IEEE Transactions on Evolutionary Computation 10(4), 392-404.
    • Paenke, I., J. Branke, and Y. Jin (2006). Efficient search for robust solutions by means of evolutionary algorithms and fitness approximation. IEEE Transactions on Evolutionary Computation 10(4), 405-420.
    • Pantelides, C. P. and S. Ganzerli (1998). Design of trusses under uncertain loads using convex models. Journal of Structural Engineering 124(3), 318-329.
    • Sano, Y. and H. Kita (2000). Optimization of noisy fitness functions by means of genetic algorithms using history of search. In M. Schoenauer, K. Deb, G. Rudolph, X. Yao, E. Lutton, J. J. Merelo, and H.-P. Schwefel (Eds.), Parallel Problem Solving from Nature, Volume 1917 of LNCS, pp. 571-580. Springer.
    • Schmidt, C., J. Branke, and S. Chick (2006). Intgrating techniques from statistical ranking into evolutionary algorithms. In F. R. et al. (Ed.), Applications of Evolutionary Computing, Volume 3907 of LNCS, pp. 752-763. Springer.
    • Sebald, A. V. and D. B. Fogel (1992). Design of fault tolerant neural networks for pattern classification. In D. B. F. . W. Atmar (Ed.), First Annual Conference on Evolutionary Programming, pp. 90 - 99. La Jolla, California 1992 , Evolutionary Programming Society.
    • Stagge, P. (1998). Averaging efficiently in the presence of noise. In A. E. Eiben, T. Ba¨ck, M. Schoenauer, and H.-P. Schwefel (Eds.), Parallel Problem Solving from Nature V, Volume 1498 of LNCS, pp. 188-197. Springer.
    • Thompson, A. (1998). On the automatic design of robust elektronics through artificial evolution. In M. Sipper, D. Mange, and A. Peres-Urike (Eds.), International Conference on Evolvable Systems, pp. 13 - 24. Springer.
    • Tjornfelt-Jensen, M. and T. K. Hansen (1999). Robust solutions to job shop problems. In Congress on Evolutionary Computation, Volume 2, pp. 1138-1144. IEEE.
    • Tsutsui, S. and A. Ghosh (1997). Genetic algorithms with a robust solution searching scheme. IEEE Transactions on Evolutionary Computation 1(3), 201-208.
    • Wiesmann, D., U. Hammel, and T. Ba¨ck (1998). Robust design of multilayer optical coatings by means of evolutionary algorithms. IEEE Transactions on Evolutionary Computation 2(4), 162-167.
    • Zitzler, E., K. Deb, and L. Thiele (2000). Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation 8(2), 173-195.
    • Zitzler, E. and S. Ku¨ nzli (2004). Inicator-based selection in multiobjective search. In Parallel Problem Solving from Nature, pp. 832-842. Springer.
    • Zitzler, E., M. Laumanns, and L. Thiele (2002). SPEA2: Improving the strength Pareto evolutionary algorithm for multi-objective optimization. In K. Giannakoglu et al. (Eds.), Evolutionary Methods for Design, Optimization and Control. CIMNE.
    • Zitzler, E., L. Thiele, M. Laumanns, C. M. Fonseca, and V. G. da Fonseca (2003). Performance assessment of multiobjective optimizers: An analysis and review. IEEE Transactions on Evolutionary Computation 7(2), 117-132.
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