On the use of metamodel-assisted, multi-objective evolutionary algorithms

References

• Benoudjit , N. , Archambeau , C. , Lendasse , A. , Lee , J. and Verleysen , M. Width optimization of the Gaussian kernels in radial basis function networks . ESANN 2002—European Symposium on Artificial Neural Networks . Bruges, Belgium. pp. 425 – 432 .
   Google Scholar
• Büche , D. , Schraudolph , N. and Koumoutsakos , P. 2005 . Accelerating evolutionary algorithms with Gaussian process fitness function models . IEEE Transactions on Systems, Man, and Cybernetics—Part C: Applications and Reviews , 35 : 183 – 194 .
   Web of Science ®Google Scholar
• Cook , P. H. , McDonald , M. A. and Firmin , M. C. 1979 . “ Airfoil RAE 2822—Pressure distributions and boundary layer and wake measurements ” . AGARD-AR-138 (Experimental Data Base for Computer Program Assessment), AGARD, May
   Google Scholar
• Cormen , T. H. , Leiserson , C. E. , Rivest , R. L. and Stein , C. 2002 . Introduction to Algorithms , (2nd edn) , Cambridge, MA : The MIT Press .
   Google Scholar
• Deb , K. , Agrawal , S. , Pratap , A. and Meyarivan , T. 2000 . “ A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II ” . In Parallel Problem Solving from Nature—PPSN VI , Edited by: Schoenauer , M. , Deb , K. , Rudolph , G. , Yao , X. , Lutton , E. , Merelo , J. J. and Schwefel , H.-P. 849 – 858 . Berlin : Springer .
   Google Scholar
• Drela , M. and Giles , M. B. 1987 . Viscous–inviscid analysis of transonic and low Reynolds number airfoils . AIAA Journal , 25 : 1347 – 1355 .
   Web of Science ®Google Scholar
• Fonseca , C. M. and Fleming , P. J. Genetic algorithms for multiobjective optimization: formulation, discussion and generalization . Proceedings of the Fifth International Conference on Genetic Algorithms . pp. 416 – 423 . San Mateo, CA : Morgan Kaufmann Publishers .
   Google Scholar
• Fritzke , B. 1994a . “ Supervised learning with growing cell structures ” . In Advances in Neural Information Processing Systems , Edited by: Cowan , J. D. , Tesauro , G. and Alspector , J. Vol. 6 , 255 – 262 . San Mateo, CA : Morgan Kaufmann .
   Google Scholar
• Fritzke , B. 1994b . Fast learning with incremental RBF networks . Neural Processing Letters , 1 : 2 – 5 .
   Web of Science ®Google Scholar
• Giannakoglou , K. C. Designing turbomachinery blades using evolutionary methods . ASME Turbo Expo '99 . June , Indianapolis, USA. ASME Paper 99-GT-181
   Google Scholar
• Giannakoglou , K. C. , Giotis , A. P. and Karakasis , M. K. 2001 . Low-cost genetic optimization based on inexact pre-evaluations and the sensitivity analysis of design parameters . Journal of Inverse Problems in Engineering , 9 : 389 – 412 .
   Web of Science ®Google Scholar
• Giles , M. B. and Drela , M. 1987 . A two-dimensional transonic aerodynamic design method . AIAA Journal , 25 : 1199 – 1206 .
   Web of Science ®Google Scholar
• Giotis , A. P. , Emmerich , M. , Naujoks , B. , Giannakoglou , K. C. and Bäck , T. 2002 . “ Low-cost stochastic optimization for engineering applications ” . In Evolutionary Methods for Design, Optimisation and Control with Applications to Industrial Problems , Edited by: Giannakoglou , K. C. , Tsahalis , D. T. , Périaux , J. , Papailiou , K. D. and Fogarty , T. 361 – 367 . Barcelona : CIMNE . In
   Google Scholar
• Haykin , S. 1999 . Neural Networks: A Comprehensive Foundation , (2nd edn) , New Jersey : Prentice Hall .
   Google Scholar
• Horn , J. and Nafpliotis , N. 1993 . “ Multiobjective optimization using the niched Pareto genetic algorithm ” . Report number = 93005, University of Illinois, Illinois Genetic Algorithm Lab., Urbana, Illinois, USA
   Google Scholar
• Karakasis , M. K. and Giannakoglou , K. C. 2003 . Inexact information aided, low-cost, distributed genetic algorithms for aerodynamic shape optimization . International Journal for Numerical Methods in Fluids , 43 : 1149 – 1166 .
   Web of Science ®Google Scholar
• Karakasis , M. K. and Giannakoglou , K. C. On the use of surrogate evaluation models in multi-objective evolutionary algorithms . ECCOMAS 2004 . Jÿvaskÿla, Finland.
   Google Scholar
• Karayiannis , N. B. and Glenn , W. M. 1997 . Growing radial basis neural networks: Merging supervised and unsupervised learning with network growth techniques . IEEE Transactions on Neural Networks , 8 : 1492 – 1506 .
   PubMed Web of Science ®Google Scholar
• Knuth , D. 1997 . The Art of Computer Programming, vol 1: Fundamental Algorithms , (3rd edn) , Reading, MA : Addison-Wesley .
   Google Scholar
• Kohonen , T. 1997 . Self-Organizing Maps , (2nd edn) , Berlin : Springer .
   Google Scholar
• Michalewicz , Z. and Fogel , D. B. 2004 . How to Solve it: Modern Heuristics , (2nd edn) , Berlin : Springer .
   Google Scholar
• Moody , J. and Darken , C. J. 1989 . Fast learning in networks of locally-tuned processing units . Neural Computation , 1 : 281 – 294 .
   Web of Science ®Google Scholar
• Morse , J. N. 1980 . Reducing the size of the nondominated set: Pruning by clustering . Computers and Operations Research , 7 : 55 – 66 .
   Web of Science ®Google Scholar
• Ong , Y. S. , Nair , P. B. and Keane , A. J. 2003a . Evolutionary optimization of computationally expensive problems via surrogate modeling . AIAA Journal , 41 : 687 – 696 .
   Web of Science ®Google Scholar
• Ong , Y. S. , Lum , K. Y. , Nair , P. B. , Shi , D. M. and Zhang , Z. K. Global convergence of unconstrained and bound constrained surrogate-assisted evolutionary search in aerodynamic shape design . Proceedings of the 2003 Congress on Evolutionary Computation (CEC '03) . Canberra, Australia. Vol. 3 , pp. 1856 – 1863 .
   Google Scholar
• Park , J. and Sandberg , I. W. 1991 . Universal approximation using radial-basis-function networks . Neural Computation , 3 : 246 – 257 .
   PubMed Web of Science ®Google Scholar
• Poggio , T. and Girosi , F. Networks for approximation and learning . Proceedings of the IEEE . Vol. 78 , pp. 1481 – 1497 .
   Google Scholar
• Rosenman , M. A. and Gero , J. S. 1985 . Reducing the Pareto optimal set in multicriteria optimization . Engineering Optimization , 8 : 189 – 206 .
   Web of Science ®Google Scholar
• Shyy , W. , Papila , N. , Vaidyanathan , R. and Tucker , K. 2001 . Global design optimization for aerodynamics and rocket propulsion components . Progress in Aerospace Sciences , 37 : 59 – 118 .
   Web of Science ®Google Scholar
• Srinivas , N. and Deb , K. 1995 . Multiobjective optimization using nondominated sorting in genetic algorithms . Evolutionary Computation , 2 : 221 – 248 .
   Web of Science ®Google Scholar
• Tikhonov , A. and Arsénine , V. 1976 . Méthodes de Résolution de problèmes mal posés , Moscow : Editions MIR .
   Google Scholar
• Tikhonov , A. N. , Goncharsky , A. V. , Stepanov , V. V. and Yagola , A. G. 1995 . Numerical Methods for the Solution of Ill-Posed Problems , Dordrecht : Kluwer Academic Publishers .
   Google Scholar
• Ulmer , H. , Streichert , F. and Zell , A. Evolution strategies assisted by Gaussian processes with improved pre-selection criterion . Proceedings of the 2003 Congress on Evolutionary Computation (CEC '03) . Canberra, Australia. Vol. 1 , pp. 692 – 699 .
   Google Scholar
• Zitzler , E. and Thiele , L. 1999 . Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach . IEEE Transactions on Evolutionary Computation , 3 : 257 – 271 .
   Web of Science ®Google Scholar
• Zitzler , E. , Deb , K. and Thiele , L. 1999 . “ Comparison of multiobjective evolutionary algorithms ” . Swiss Federal Institute of Technology (ETH), Computer Engineering and Communication Networks Lab.(TIK), Zurich, Switzerland, number 70, December
   Google Scholar
• Zitzler , E. , Laumans , M. and Thiele , L. 2001 . “ SPEA2: Improving the Strength Pareto Evolutionary Algorithm ” . Swiss Federal Institute of Technology (ETH), Computer Engineering and Communication Networks Lab.(TIK), Zurich, Switzerland, number 103, May
   Google Scholar
• Zitzler , E. , Laumans , M. and Thiele , L. 2002 . “ SPEA2: Improving the Strength Pareto Evolutionary Algorithm for multiobjective optimization ” . In Evolutionary Methods for Design, Optimisation and Control with Applications to Industrial Problems , Edited by: Giannakoglou , K. C. , Tsahalis , D. T. , Périaux , J. , Papailiou , K. D. and Fogarty , T. 19 – 26 . Barcelona : CIMNE .
   Google Scholar