1. Basseur, M., Zitzler, E.: A preliminary study on handling uncertainty in indicator-based multiobjective optimization. In: Rothlauf, F., et al. (eds.) EvoWorkshops 2006. LNCS, vol. 3907, pp. 727–739. Springer, Heidelberg (2006)

   Chapter  Google Scholar 

2. Büche, D., Stoll, P., Dornberger, R., Koumoutsakos, P.: Multiobjective evolutionary algorithm for optimization of noisy combustion processes. IEEE Transactions on Systems, Man, and Cybernetics - Part C: Applications and Reviews32(4), 460–473 (2002)

   Article  Google Scholar 

3. Bui, L., Abbass, H., Essam, D.: Fitness inheritance for noisy evolutionary multi-objective optimization. In: Proceeding of the Genetic and Evolutionary Computation Conference, pp. 779–785 (2005)

   Google Scholar 

4. Das, S., Konar, A., Chakraborty, U.K.: Improved differential evolution algorithms for handling noisy optimization problems. In: IEEE Congress on Evolutionary Computation, vol. 2, pp. 1691–1698. IEEE (2005)

   Google Scholar 

5. Di Pietro, A., While, L., Barone, L.: Applying Evolutionary Algorithms to Problems with Noisy, Time-consuming Fitness Functions. In: IEEE Congress on Evolutionary Computation, vol. 2, pp. 1254–1261. IEEE (2004)

   Google Scholar 

6. Eskandari, H., Geiger, C.D.: Evolutionary multiobjective optimization in noisy problem environments. Journal of Heuristics15, 559–595 (2009)

   Article MATH  Google Scholar 

7. Fieldsend, J.E., Everson, R.M.: Efficiently identifying pareto solutions when objective values change. In: Proceedings of the 2014 Conference on Genetic and Evolutionary Computation, pp. 605–612. ACM (2014)

   Google Scholar 

8. Fieldsend, J.E., Everson, R.M.: Multi-objective optimisation in the presence of uncertainty. In: IEEE Congress on Evolutionary Computation, pp. 243–250 (2005)

   Google Scholar 

9. Fieldsend, J.E., Everson, R.M.: On the efficient use of uncertainty when performing expensive ROC optimisation. In: IEEE Congress on Evolutionary Computation, pp. 3984–3991 (2008)

   Google Scholar 

10. Fieldsend, J.E., Everson, R.M.: The Rolling Tide Evolutionary Algorithm: A Multi-Objective Optimiser for Noisy Optimisation Problems. IEEE Transactions on Evolutionary Computation (in press).http://dx.doi.org/10.1109/TEVC.2014.2304415

11. Fieldsend, J.E., Everson, R.M., Singh, S.: Using unconstrained elite archives for multi-objective optimisation. IEEE Transactions on Evolutionary Computation7, 305–323 (2001)

    Article  Google Scholar 

12. Goh, C.-K., Tan, K.C.: An investigation on noisy environments in evolutionary multiobjective optimization. IEEE Transactions on Evolutionary Computation11(3), 354–381 (2007)

    Article  Google Scholar 

13. Goh, C.-K., Tan, K.C.: Evolutionary Multi-objective Optimization in Uncertain Environments. SCI, vol. 186. Springer, Heidelberg (2009)

    Google Scholar 

14. Hanne, T.: On the convergence of multi objective evolutionary algorithms. European Journal of Operational Research117, 553–564 (1999)

    Article MATH  Google Scholar 

15. Horn, J., Nafpliotis, N.: Multiobjective Optimization Using the Niched Pareto Genetic Algorithm. Technical Report 93005, Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign (1993)

    Google Scholar 

16. Hughes, E.J.: Constraint handling with uncertain and noisy multi-objective evolution. In: IEEE Congress on Evolutionary Computation, pp. 963–970 (2001)

    Google Scholar 

17. Hughes, E.J.: Evolutionary multi-objective ranking with uncertainty and noise. In: Zitzler, E., Deb, K., Thiele, L., Coello Coello, C.A., Corne, D.W. (eds.) EMO 2001. LNCS, vol. 1993, pp. 329–343. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

18. Jin, Y., Branke, J.: Evolutionary optimization in uncertain environments-a survey. IEEE Transactions on Evolutionary Computation9(3), 303–317 (2005)

    Article  Google Scholar 

19. Knowles, J.D., Thiele, L., Zitzler, E.: A tutorial on the performance assessment of stochastic multiobjective optimizers. Technical Report 214, Computer Engineering and Networks Laboratory, ETH Zurich, Switzerland (2006)

    Google Scholar 

20. Park, T., Ryu, K.R.: Accumulative sampling for noisy evolutionary multi-objective optimization. In: Proceeding of the Genetic and Evolutionary Computation Conference, pp. 793–800 (2011)

    Google Scholar 

21. Robič, T., Filipič, B.: DEMO: Differential evolution for multiobjective optimization. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 520–533. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

22. Schutze, O., Esquivel, X., Lara, A., Coello Coello, C.A.: Using the Averaged Hausdorff Distance as a Performance Measure in Evolutionary Multiobjective Optimization. IEEE Transactions on Evolutionary Computation16(4), 504–522 (2012)

    Article  Google Scholar 

23. Shim, V.A., Tan, K.C., Chia, J.Y., Al Mamun, A.: Multi-objective Optimization with Estimation of Distribution Algorithm in a Noisy Environment. Evolutionary Computation21(1), 149–177 (2013)

    Article  Google Scholar 

24. Siegmund, F.: Sequential sampling in noisy multi-objective evolutionary optimization. Master’s thesis, University of Skövde, School of Humanities and Informatics, Sweden (2009)

    Google Scholar 

25. Siegmund, F., Ng, A., Deb, K.: A comparative study of dynamic resampling strategies for guided evolutionary multi-objective optimization. In: IEEE Congress on Evolutionary Computation (CEC), pp. 1826–1835. IEEE (2013)

    Google Scholar 

26. Syberfeldt, A., Ng, A., John, R.I., Moore, P.: Evolutionary optimisation of noisy multi-objective problems using confidence-based dynamic resampling. European Journal of Operational Research204, 533–544 (2010)

    Article MATH MathSciNet  Google Scholar 

27. Teich, J.: Pareto-front exploration with uncertain objectives. In: Zitzler, E., Deb, K., Thiele, L., Coello Coello, C.A., Corne, D.W. (eds.) EMO 2001. LNCS, vol. 1993, pp. 314–328. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

28. van Veldhuizen, D., Lamont, G.: Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art. Evolutionary Computation8(2), 125–147 (2000)

    Article  Google Scholar 

29. Villa, C., Lozinguez, E., Labayrade, R.: Multi-objective optimization under uncertain objectives: application to engineering design problem. In: Purshouse, R.C., Fleming, P.J., Fonseca, C.M., Greco, S., Shaw, J. (eds.) EMO 2013. LNCS, vol. 7811, pp. 796–810. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

30. Yang, S., Ong, Y.S., Jin, Y.: Evolutionary computation in dynamic and uncertain environments. SCI, vol. 51. Springer, Heidelberg (2007)

    Google Scholar 

31. Zhang, Q., Zhou, A., Zhao, S., Suganthan, P.N., Liu, W., Tiwari, S.: Multiobjective optimization Test Instances for the CEC 2009 Special Session and Competition. Technical Report CES-487, School of Computer Science and Electronic Engineering, University of Essex, UK, April 2009

    Google Scholar 

Download references