Surrogate based EA for expensive optimization problems

2007 IEEE Congress on Evolutionary Computation, CEC 2007

Volumn , Issue , 2007, Pages 3847-3857

  Bhattacharya, Maumita ^a,b  

^a IEEE

^b CHARLES STURT UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

DYNAMIC APPROXIMATE FITNESS; FITNESS FUNCTION EVALUATION; METAMODELING;

APPROXIMATION THEORY; EVOLUTIONARY ALGORITHMS; FUNCTION EVALUATION; MATHEMATICAL MODELS; OPTIMIZATION; SUPPORT VECTOR MACHINES;

PROBLEM SOLVING;

EID: 79955182339     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CEC.2007.4424972     Document Type: Conference Paper

References (27)

1. A. Ratle., "Accelerating the convergence of evolutionary algorithms by fitness landscape approximation", Parallel Problem Solving from Nature-PPSN V, Springer-Verlag, pp. 87-96, 1998.
2. A. Smola and B. Schölkopf, "A Tutorial on Support Vector Regression", NeuroCOLT Technical Report NC-TR-98-030, Royal Holloway College, University of London, UK, 1998.
3. B. Dunham, D. Fridshal., R. Fridshal and J. North, "Design by natural selection", Synthese, 15, pp. 254-259, 1963.
4. B. Schölkopf, J. Burges and A. Smola, ed., "Advances in Kernel Methods: Support VectorMachines", MIT Press, 1999.
5. C. Bishop, "Neural Networksfor Pattern Recognition", Oxford Press, 1995.
6. D. Büche., N. Schraudolph, and P. Koumoutsakos, "Accelerating Evolutionary Algorithms Using Fitness Function Models", Proc. Workshops Genetic and Evolutionary Computation Conference, Chicago, 2003.
7. H. D. Vekeria and i. C. Parmee, "The use of a co-operative multi-level CHC GA for structural shape optimization", Fourth European Congress on Intelligent Techniques and Soft Computing - EUFIT'96, 1996.
8. H. S. Kim and S. B. Cho, " An efficient genetic algorithm with less fitness evaluation by clustering", Proceedings of IEEE Congress on Evolutionary Computation, pp. 887-894, 2001.
9. J. Sacks, W. Welch, T. Mitchell and H. Wynn, "Design and analysis of computer experiments", Statistical Science, 4(4), 1989.
10. K. Rasheed, "An Incremental-Approximate-Clustering Approach for Developing Dynamic Reduced Models for Design Optimization", Proceedings of IEEE Congress on Evolutionary Computation, 2000.
11. K. Rasheed, S. Vattam and X. Ni., "Comparison of .Methods for Using Reduced Models to Speed Up Design Optimization", The Genetic and Evolutionary Computation Conference (GECCO'2002), 2002.
12. K. Won, T. Roy and K. Tai, "A Framework for Optimization Using Approximate Functions", Proceedings of the IEEE Congress on Evolutionary Computation' 2003, Vol.3, IEEE Catalogue No. 03TH8674C, ISBN 0-7803-7805-9.
13. M. A. El-Beltagy and A. J. Keane, "Evolutionary optimization for computationally expensive problems using Gaussian processes", Proc. Int. Conf. on Artificial Intelligence (IC-AT2001), CSREA Press, Las Vegas, pp. 708-714, 2001.
14. M. Bhattacharya and G. Lu, "DAFHEA: A Dynamic Approximate Fitness based Hybrid Evolutionary Algorithm", Proceedings of the IEEE Congress on Evolutionary Computation' 2003, Vol.3, IEEE Catalogue No. 03TH8674C, ISBN 0-7803-7805-9, pp. 1879-1886.
15. M. Bhattacharya, "Surrogate based Evolutionary Algorithm for Engineering Design Optimization", Proceedings of the Eighth International Conference on Cybernetics, Informatics and Systemic (ICCIS 2005), ISBN 975-98458-9-X, pp. 52-57.
16. P. Hajela and A. Lee., "Topological optimization of rotorcraft subfloor structures for crashworthiness considerations", Computers and Structures, vol.64, pp. 65-76, 1997.
17. R. Myers and D. Montgomery, "Response Surface Methodology", John Wiley & Sons, 1985.
18. S. Pierret, "Three-dimensional blade design by means of an artificial neural network and Navier-Stokes solver", Proceedings of Fifth Conference on Parallel Problem Solving from Nature, Amsterdam, 1999.
19. S. R. Gunn, "Support Vector Machines for Classification and Regression", Technical Report, School of Electronics and Computer Science, University of Southampton, (Southampton, U.K.), 1998.
20. T. Hastie, R. Tibshirani, J. Friedman, "The Elements of Statistical Learning: Data Mining, Inference, and Prediction", Springer Series in Statistics, ISBN 0-387-95284-5.
21. V. Cherkassky and Y. Ma, "Multiple Model Estimation: A New Formulation for Predictive Learning", under review in IEE Transaction on Neural Network.
22. V. Torczon and M. W. Trosset, "Using approximations to accelerate engineering design optimisation", ICASE Report No. 98-33. Technical report, NASA Langley Research Center Hampton, VA 23681-2199, 1998.
23. V. V. Toropov, a. A. Filatov and A. A. Polykin, "Multiparameter structural optimization using FEM and multipoint explicit approximations", Structural Optimization, vol. 6, pp. 7-14, 1993.
24. V. Vapnik, "The Nature of Statistical Learning Theory", Springer-Verlag, NY, USA, 1999.
25. Y. Jin, M. Olhofer and B. Sendhoff, "A Framework for Evolutionary Optimization with Approximate Fitness Functions", IEEE Transactions on Evolutionary Computation, 6(5), pp. 481-494, (ISSN: 1089-778X). 2002.
26. Y. Jin, M. Olhofer and B. Sendhoff., "On Evolutionary Optimisation with Approximate Fitness Functions", Proceedings of the Genetic and Evolutionary Computation Conference GECCO, Las Vegas, Nevada, USA. pp. 786- 793, July 10-12, 2000.
27. Y. Jin, "A Comprehensive Survey of Fitness Approximation in Evolutionary Computation", Soft Computing, 9(1), pp.3-12, 2005.