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Branke, J., Deb, K., Dierolf, H., and Osswald M.: Finding Knees in Multi- objective Optimization. In Proceedings of Parallel Problem Solving from Nature (PPSN 2004). Springer, 2004, pp. 722-731.
Branke, J., and Schmidt, C.: Faster convergence by means of fitness estimation. In Soft Computing Journal. (in press).
Chen, J., Goldberg, D. E., Ho, S., and Sastry, K.: Fitness inheritance in multi- objective optimization. In Proceedings, Genetic and Evolutionary computation Conference, 2002. Morgan Kaufmann, 2002, pp. 319-326.
Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. First Edition, Chichester, Uk: Wiley, 2001.
Deb, K., Agrawal, S., Pratap, A., and Meyarivan, T.: A fast and elitist multi- objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Com- putation, 6(2):182-197, 2002.
Deb, K., Thiele, L., Laumanns, M., and Zitzler, E.: Scalable test problems for evolutionary multi-objective optimization. In Abraham, A., Jain, L., and Goldberg, R., editors, Evolutionary Multiobjective Optimization, 2005, pages 105-145. London: Springer-Verlag. 13 EMO with Successive Meta-models 321
Deb, K., Mohan, M., and Mishra, S.: Towards a quick computation of well- spread Pareto-optimal solutions. In Proceedings, Evolutionary Multi-Criterion Optimization, 2003. Springer, 2003, pp. 226-236.
Deb, K., and Jain, S.: Running performance Metrics for evolutionary multi- objective optimization. In Proceedings, Fourth Asia-Pacific Conference on Sim- ulated Evolution and Learning (SEAL’02). (Singapore), 2002, pp. 13-20.
Deb, K. and Gupta, H.: Searching for robust Pareto-optimal solutions in multi- objective optimization. In Proceedings of Evolutionary Multi-Criterion Opti- mization (EMO 2005), Springer-Verlag, 2005, pp. 150-164.
Eby, D., Averill, R. C., Punch III, W. F., and Goodman, E. D.: Evaluation of injection island GA performance on flywheel design optimization. In Proceed- ings, Third Conference on Adaptive Computing in Design and Manufacturing. Springer, 1998.
El-Beltagy, M. A., Nair, P. B., and Keane, A. J.: Metamodeling techniques for evolutionary optimization of computationally expensive problems: promises and limitations. In Proceedings of the Genetic and Evolutionary Computation Conference, 1999. Morgan Kaufman, 1999, pp. 196-203.
Emmerich, M., Giotis, A., Ozdenir, M., Back, T., and Giannakoglou, K.: Metamodel-assisted evolution strategies. In Proceedings, Parallel Problem Solv- ing from Nature, 2002. Springer, 2002, pp. 371-380.
Emmerich, M., and Naujoks, B.: Metamodel assisted multi-objective optimiza- tion strategies and their application in airfoil design. In Proceedings, Adaptive Computing in Design and Manufacture VI, 2004. Springer, 2004, pp. 249-260.
Fonseca, C. M. and Fleming, P. J.: On the performance assessment and compar- ison of stochastic multiobjective optimizers. In Proceedings of Parallel Problem Solving from Nature IV (PPSN-IV), Springer, 1996, pp. 584-593.
Farina, M.: A neural network based generalized response surface multi-objective evolutionary algorithms. In Congress on Evolutionary Computation, 2002. IEEE Press, 2002, pp. 956-961.
Giannakoglou, K. C.: Design of optimal aerodynamic shapes using stochastic optimization methods and computational intelligence. In Progress in Aerospace Science, Vol. 38, pp. 43-76, 2002.
Giotis, A. P., and Giannakoglou, K. C.: Chapter 23: low cost GAs assisted by ANNs - applications in turbomachinery. edited by Periaux, J. et al., Jhon Wiley & Sons, (to appear).
Goldberg, D. E., Deb, K., and Clark, J. H.: Genetic algorithms, noise, and the sizing of populations. Complex System, 6, pp. 333-362, 1992.
Haykin, S.: Neural networks a comprehensive foundation. second edition, Singa- pore: Addison Wesley, 2001. pp. 208.
Jin, Y., Olhofer, M., and Sendhoff, B.: A framework for evolutionary optimiza- tion with approximate fitness functions. In IEEE Transactions on Evolutionary Computation, 6(5), pp. 481-494, 2002.
Jin, Y., and Sendhoff, B.: Fitness approximation in evolutionary computation - A survey. In Proceedings, Genetic and Evolutionary Computation Conference, 2002. Morgan Kaufmann, 2002, pp. 1105-1112.
Jin, Y., and Sendhoff, B.: Reducing fitness evaluations using clustering tech- niques and neural network ensembles. In Proceedings, Genetic and Evolutionary Computation Conference, 2004. Springer, 2004, pp. 688-699. 322 Kalyanmoy Deb and Pawan K.S. Nain
Nain, P. K. S., and Deb, K.: Computationally effective search and optimiza- tion procedure using coarse to fine approximation. In Proceedings, Congress on Evolutionary Computation, 2003. IEEE Computer Society Press, 2003, pp. 2081-2088.
Nair, P. B., Keane, A. J., and Shimpi, R. P.: Combining approximation concepts with genetic algorithm based structural optimization procedures. In Proceedings of the 39th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, 1998, pp. 1741-1751
Poloni, C., Giurgevich, A., Onesti, L., and Prdiroda, V.: Hybridization of a multi-objective genetic algorithm, a neural network and a classical optimizer for a complex design problem in fluid dynamics. In Computer Methods in Applied Mechanics and Engineering, volume 186, 2000, pp. 403-420.
Rasheed, K., and Hirsh, H.: Informed operators: speeding up genetic-algorithm- based design optimization using reduced models. In Proceedings, Genetic and Evolutionary Computation Conference, 2000. Morgan Kaufmann, 2000, pp. 628- 635.
Rasheed, K., Vattam, S., and Ni, X.: Comparison of methods for using reduced models to speed up design optimization. In Proceedings, Genetic and Evolution- ary Computation Conference, 2002. Morgan Kaufmann, 2002, pp. 1180-1187.
Ratle, A.: Accelerating the convergence of evolutionary algorithms by fitness landscape approximation. In Proceedings, Parallel Problem Solving from Nature, 1998. volume V, 1998, pp. 87-96.
Reklaitis, G. V., Ravindran, A. and Ragsdell, K. M. (1983). Engineering Opti- mization Methods and Applications. New York : Wiley.
Sastry, K., Goldberg, D. E., and Pelikan, M.: Don’t evaluate, inherit. In Proceed- ings, Genetic and Evolutionary computation Conference, 2001. Morgan Kauf- mann, 2001, pp. 551-558.
Sefrioui, M., and Périaux, J.: A hierarchical genetic algorithm using multiple models for optimization. In Proceedings, 6th International Conference on Par- allel Problem Solving from Nature - PPSN VI . Lecture Notes in Computer Science 1917, Springer 2000.
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Deb, K., Nain, P.K.S. (2007). An Evolutionary Multi-objective Adaptive Meta-modeling Procedure Using Artificial Neural Networks. In: Yang, S., Ong, YS., Jin, Y. (eds) Evolutionary Computation in Dynamic and Uncertain Environments. Studies in Computational Intelligence, vol 51. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-49774-5_13
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