Distributed evolutionary algorithms with hierarchical evaluation

References

• Alba , E. and Tomassini , M. 2002 . Parallelism and evolutionary algorithms . IEEE Transactions on Evolutionary Computation , 6 ( 5 ) : 443 – 462 .
   Web of Science ®Google Scholar
• Auger , A. and Hansen , N. 2005 . A restart CMA evolution strategy with increasing population size . The 2005 IEEE congress on evolutionary computation , 2 : 1769 – 1776 .
   Google Scholar
• Coello Coello , C. , Van Veldhuizen , D. and Lamont , G. B. 2002 . Evolutionary algorithms for solving multi-objective problems , New York : Kluwer Academic .
   Google Scholar
• Deb , K. 2001 . Multi-objective optimization using evolutionary algorithms , Chichester : Wiley .
   Google Scholar
• Deb , K. 2002 . A fast and elitist multiobjective genetic algorithm: NSGA-II . IEEE Transactions on Evolutionary Computation , 6 ( 2 ) : 182 – 197 .
   Web of Science ®Google Scholar
• Désidéri , J. and Janka , A. 2003 . “ Hierarchical parametrization for multilevel evolutionary shape optimization with application to aerodynamics ” . In International congress on evolutionary methods for design, optimization and control with applications to industrial problems (EUROGEN 2003) Edited by: Bugeda , G. , Désidéri , J. , Periaux , J. , Schoenauer , M. and Winter , G.
   Google Scholar
• Doorly , D. J. and Peiró , J. Supervised parallel genetic algorithms in aerodynamic optimisation . 13th AIAA computational fluid dynamics conference . Snowmass Village, CO . AIAA-1997-1852
   Google Scholar
• Doorly , D. J. , Peiró , J. and Spooner , S. Design optimisation using distributed evolutionary methods . 37th Aerospace Sciences Meeting and Exhibit . Reno, NV. AIAA-1999-111
   Google Scholar
• Drela , M. and Giles , M. B. 1987 . Viscous–inviscid analysis of transonic and low Reynolds number airfoils . AIAA Journal , 25 ( 10 ) : 1347 – 1355 .
   Web of Science ®Google Scholar
• Duvigneau , R. , Chaigne , B. and Desideri , J. 2006 . Multi-level parametrization for shape optimization in aerodynamics and electromagnetics using a particle swarm optimization algorithm . Technical report RR-6003, INRIA
   Google Scholar
• Eby , D. Evaluation of injection island GA performance on flywheel design optimization . Proceedings of the 3rd conference on adaptive computing in design and manufacturing . Plymouth, UK. pp. 121 – 136 . Springer-Verlag .
   Google Scholar
• Emmerich , M. , Giannakoglou , K. and Naujoks , B. 2006 . Single and multi-objective evolutionary optimization assisted by gaussian random field metamodels . IEEE Transactions on Evolutionary Computation , 10 ( 4 ) : 421 – 439 .
   Web of Science ®Google Scholar
• Giannakoglou , K. C. 2002 . Design of optimal aerodynamic shapes using stochastic optimization methods and computational intelligence . Progress in Aerospace Sciences , 38 ( 1 ) : 43 – 76 .
   Web of Science ®Google Scholar
• Haykin , S. 1999 . Neural networks: a comprehensive foundation , Upper Saddle River, NJ : Prentice-Hall .
   Google Scholar
• Herrera , F. and Lozano , M. 2000 . Gradual distributed real-coded genetic algorithms . IEEE Transactions on Evolutionary Computation , 4 ( 1 ) : 43 – 63 .
   Web of Science ®Google Scholar
• Herrera , F. , Lozano , M. and Moraga , C. 1999 . Hierarchical distributed genetic algorithms . International Journal of Intelligent Systems , 14 ( 9 ) : 1099 – 1121 .
   Google Scholar
• Jin , Y. 2005 . A Comprehensive survey of fitness approximation in evolutionary computation . Soft Computing Journal – A Fusion of Foundations, Methodologies and Applications , 9 ( 1 ) : 3 – 12 .
   Google Scholar
• Kampolis , I. 2007 . “ Multilevel optimization strategies based on metamodel–assisted evolutionary algorithms, for computationally expensive problems ” . 4116 – 4123 . Singapore, New York : IEEE Press . 2007 Congress on Evolutionary Computation (CEC07)
   Google Scholar
• Kampolis , I. and Giannakoglou , K. 2008 . A Multilevel approach to single- and multiobjective aerodynamic optimization . Computer Methods in Applied Mechanics and Engineering , 197 ( 33–40 ) : 2963 – 2975 .
   Web of Science ®Google Scholar
• Karakasis , M. and Giannakoglou , K. 2003 . Inexact information aided, lLow-cost, distributed genetic algorithms for aerodynamic shape optimization . International Journal for Numerical Methods in Fluids , 43 ( 10–11 ) : 1149 – 1166 .
   Web of Science ®Google Scholar
• Karakasis , M. and Giannakoglou , K. 2005 . On the use of metamodel-assisted, multi-objective evolutionary algorithms . Engineering Optimization , 38 ( 8 ) : 941 – 957 .
   Web of Science ®Google Scholar
• Karakasis , M. , Koubogiannis , D. and Giannakoglou , K. 2007 . Hierarchical distributed evolutionary algorithms in shape optimization . International Journal for Numerical Methods in Fluids , 53 : 455 – 469 .
   Google Scholar
• Knowles , J. and Corne , D. M-PAES: A memetic algorithm for multiobjective optimization . Proceedings of the 2000 congress on evolutionary computation (CEC-2000) . New York:, CA. pp. 325 – 332 . IEEE Press .
   Google Scholar
• Langdo , W. and Poli , R. 2005 . “ Evolving problems to learn about particle swarm and other optimisers. (CEC-2005) ” . 81 – 88 . New York : IEEE Press .
   Google Scholar
• Lim , D. 2008 . “ Generalizing surrogate-assisted evolutionary computation ” . In IEEE Transactions on Evolutionary Computation Edinburgh in press
   Google Scholar
• Lin , S. C. , Punch , W. F. and Goodman , E. D. Coarse-grain parallel genetic algorithms: categorization and new approach . Proceedings of the 6th IEEE symposium on parallel and distributed processing . New York. pp. 28 – 37 .
   Google Scholar
• Muyl , F. , Dumas , L. and Herbert , V. 2004 . Hybrid method for aerodynamic shape optimization in automotive industry . Journal of Computers and Fluids , 33 ( 5–6 ) : 849 – 858 .
   Google Scholar
• Poloni , C. 2000 . Hybridization of a multiobjective genetic algorithm, a neural network and a classical optimizer for a complex design problem in fluid dynamics . Computer Methods in Applied Mechanics and Engineering , 186 ( 2 ) : 403 – 420 .
   Google Scholar
• Sefrioui , M. and Périaux , J. A hierarchical genetic algorithm using multiple models for optimization . Proceedings of the 6th international conference on parallel problem solving from nature (PPSN VI) . Paris. Edited by: Schoenauer , M. Deb , K. pp. 879 – 888 . Springer-Verlag . Lecture Notes in Computer Science Vol. 1917
   Google Scholar
• Srinivas , N. and Deb , K. 1995 . Multiobjective optimization using nondominated sorting in genetic Algorithms . Evolutionary Computation , 2 ( 3 ) : 221 – 248 .
   Web of Science ®Google Scholar
• Suganthan , P. 2005 . “ Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization ” . Nanyang Technological University . Technical report 2005005
   Google Scholar
• Zhou , Z. 2007 . Combining global and local surrogate models to accelerate evolutionary optimization . IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews , 37 ( 1 ) : 66 – 76 .
   Web of Science ®Google Scholar
• Zitzler , E. , Brockhoff , D. and Thiele , L. The hypervolume indicator revisited: on the design of Pareto-compliant indicators via weighted integration . Conference on evolutionary multi-criterion optimization (EMO 2007) . Berlin:. Edited by: Obayashi , S. pp. 862 – 876 . Springer-Verlag . Lecture Notes in Computer Science Vol. 4403
   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 (EUROGEN 2001) , 19 – 26 . Barcelona : CIMNE .
   Google Scholar