All publications sorted by year


J. Branke and C. Schmidt.
Fast convergence by means of fitness estimation.
Soft Computing Journal,
9(1):1320,
2005.
[bibtexkey = Branke05]

D. Bueche,
N.N. Schraudolph,
and P. Koumoutsakos.
Accelerating evolutionary algorithms with Gaussian process fitness function models.
IEEE Trans. on Systems, Man, and Cybernetics: Part C,
35(2):183194,
2005.
[bibtexkey = Bueche05]

D. Chafekar,
L. Shi,
K. Rasheed,
and J. Xuan.
Multiobjective GA optimization using reduced models.
IEEE Trans. on Systems, Man, and Cybernetics: Part C,
9(2):261265,
2005.
[bibtexkey = Chafekar05]

M. Farina and P. Amato.
Linked interpolationoptimization strategies for multicriteria optimization problems.
Soft Computing,
9(1):5465,
2005.
[bibtexkey = Farina05]

M. Hüscken,
Y. Jin,
and B. Sendhoff.
Structure optimization of neural networks for aerodynamic optimization.
Soft Computing Journal,
9(1):2128,
2005.
[bibtexkey = Huesken05]

Y. Jin and J. Branke.
Evolutionary optimization in uncertain environments: A survey.
IEEE Transactions on Evolutionary Computation,
9(3):303317,
2005.
[bibtexkey = Jin05a]

Y. Jin.
A comprehensive survey of fitness approximation in evolutionary computation.
Soft Computing Journal,
9(1):312,
2005.
[bibtexkey = Jin05b]

K. Rasheed,
X. Ni,
and S. Vattam.
Comparison of methods for developing dynamic reduced models for design optimization.
Soft Computing Journal,
9(1):2937,
2005.
[bibtexkey = Rasheed05]

L. Graening,
Y. Jin,
and B. Sendhoff.
Efficient evolutionary optimization using individualbased evolution control and neural networks: A comparative study.
In European Symposium on Artificial Neural Networks,
pages 273278,
2005.
[bibtexkey = Graening05]

J. Knowles and E. Hughes.
Multiobjective optimization on a budget of 250 evaluations.
In Evolutionary MultiCriterion Optimization,
LNCS 3410,
pages 176190,
2005.
Springer.
[bibtexkey = Knowles05]

Y. Jin,
M. Hüsken,
M. Olhofer,
and B. Sendhoff.
Neural networks for fitness approximation in evolutionary optimization.
In Y. Jin, editor,Knowledge Incorporation in Evolutionary Computation,
pages 281305.
Springer,
Berlin,
2004.
[bibtexkey = Jin04b]

Y. S. Ong,
P. B. Nair,
A. J. Keane,
and K. W. Wong.
SurrogateAssisted Evolutionary Optimization Frameworks for HighFidelity Engineering Design Problems.
In Y. Jin, editor,Knowledge Incorporation in Evolutionary Computation,
Studies in Fuzziness and Soft Computing,
pages 307332.
Springer,
2004.
[bibtexkey = Ong04]

H. Ulmer and F. Streichert and. A. Zell.
Modelassisted evolution strategies.
In Y. Jin, editor,Knowledge Incorporation in Evolutionary Computation,
pages 333357.
Springer,
Berlin,
2004.
[bibtexkey = Ulmer04a]

R.G. Regis and C.A. Shoemaker.
Local Function Approximation in Evolutionary Algorithms for the Optimization of Costly Functions.
IEEE Transactions on Evolutionary Computation,
8(5):490505,
2004.
[bibtexkey = RegisShoemaker:2004]

L. Elliott and et al.
An informed operator based genetic algorithm for tuning the reaction rate parameters of chemical kenetics mechanisms.
In Genetic and Evolutionary Computation Conference,
pages 945956,
2004.
[bibtexkey = Elliott04b]

L. Elliott and et al.
Efficient clusteringbased genetic algorithms in chemical kinetic modeling.
In Genetic and Evolutionary Computation Conference,
pages 932944,
2004.
[bibtexkey = Elliott04a]

D. Hidovic and J.E. Rowe.
Validating a model of colon colouration using an evolution strategy with adaptive approximations.
In Genetic and Evolutionary Computation Conference,
pages 10051017,
2004.
[bibtexkey = Hidovic04]

Y. Jin and B. Sendhoff.
Reducing fitness evaluations using clustering techniques and neural networks ensembles.
In Genetic and Evolutionary Computation Conference,
volume 3102 of LNCS,
pages 688699,
2004.
Springer.
[bibtexkey = Jin04]

M. Pelikan and K. Sastry.
Fitness inheritance in the Bayesian optimization algorithms.
In Genetic and Evolutionary Computation Conference,
pages 4859,
2004.
Springer.
[bibtexkey = Pelikan04]

H. Ulmer,
F. Streichert,
and A. Zell.
Evolution strategies with controlled model assistance.
In Congress on Evolutionary Computation,
pages 15691576,
2004.
IEEE.
[bibtexkey = Ulmer04]

K.S. Won and T. Ray.
Performance of Kriging and cokriging based surrogate models within the unified framework for surrogate assisted optimization.
In Congress on Evolutionary Computation,
pages 15771585,
2004.
IEEE.
[bibtexkey = Won04]

Z. Zhou,
Y.S. Ong,
and P.B. Nair.
Hierarchical surrogateassisted evolutionary optimization framework.
In Congress on Evolutionary Computation,
pages 15861593,
2004.
IEEE.
[bibtexkey = Zhou04]

H.S. Chung and J. J. Alonso.
Multiobjective optimization using approximation modelbased genetic algorithms.
Technical report 20044325,
AIAA,
2004.
[bibtexkey = ChAl04]

T. Goel,
R. Vaidyanathan,
R. Haftka,
W. Shyy,
N. Queipo,
and K. Tucker.
Response surface approximation of Pareto optimal front in multiobjective optimization.
Technical report 20044501,
AIAA,
2004.
[bibtexkey = Goel04]

Y.S. Hong,
H.Lee,
and M.J. Tahk.
Acceleration of the convergence speed of evolutionary algorithms using multilayer neural networks.
Engineering Optimization,
35(1):91102,
2003.
[bibtexkey = Hong03]

Y.S. Ong,
P.B. Nair,
and A.J. Keane.
Evolutionary optimization of computationally expensive problems via surrogate modeling.
AIAA Journal,
41(4):687696,
2003.
[bibtexkey = Ong02]

M. Salami and T. Hendtlass.
A fast evaluation strategy for evolutionary algorithms.
Applied Soft Computing,
2:156173,
2003.
[bibtexkey = Salami03]

J.S. AguilarRuiz,
D. Mateos,
and D.S. Rodriguez.
Evolutionary Neuroestimation of Fitness Functions.
In Lecture Notes on Artificial Inteligence,
volume 2902,
pages 7483,
2003.
[bibtexkey = AguilarRuiz03]

M. Bhattacharya and G. Lu.
A dynamic approximate fitness based hybrid EA for optimization problems.
In Proceedings of IEEE Congress on Evolutionary Computation,
pages 18791886,
2003.
[bibtexkey = Bhatta03]

D. Bueche.
Accelerating evolutionary algorithms using fitness function models.
In Proceedings of GECCO Workshop on Learning, Adaptation and Approximation in Evolutionary Computation,
pages 166169,
2003.
[bibtexkey = Bueche03]

E. Ducheyne,
B. De Baets,
and R. de Wulf.
Is fitness inheritance useful for realworld applications?.
In Second International Conference on Multicriterion Optimization,
LNCS 2632,
pages 3142,
2003.
Springer.
[bibtexkey = Ducheyne03]

X. Jiang,
D. Chafekar,
and K. Rasheed.
Constrained multiobjective GA optimization using reduced models.
In Proceedings of GECCO Workshop on Learning, Adaptation and Approximation in Evolutionary Computation,
pages 174177,
2003.
[bibtexkey = Jiang03]

Y. Jin,
M. Huesken,
and B. Sendhoff.
Quality measures for approximate models in evolutionary computation.
In Proceedings of GECCO Workshops: Workshop on Adaptation, Learning and Approximation in Evolutionary Computation,
Chicago,
pages 170174,
2003.
[bibtexkey = Jin03c]

Y. Jin and B. Sendhoff.
Tradeoff between performance and robustness: An multiobjective approach.
In Second International Conference on Multicriterion Optimization,
LNCS 2632,
pages 237251,
2003.
[bibtexkey = Jin03b]

A. Mutoh,
S. Kato,
T. Nakamura,
and H. Itoh.
Reducing execution time on genetic algorithms in realworld applications using fitness prediction.
In Proc. of the IEEE Congress on Evolutionary Computation,
volume 1,
Sidney, Australia,
pages 552559,
2003.
IEEE.
[bibtexkey = Mutoh03]

P. K. S. Nain and K. Deb.
Computationally effective search and optimization procedure using coarse to fine approximation.
In Congress on Evolutionary Computation,
pages 20812088,
2003.
[bibtexkey = Nain03]

Y. S. Ong,
K.Y. Lum,
P. B. Nair,
D.M. Shi,
and Z.K. Zhang.
Global Convergence Unconstrained And Bound Constrained SurrogateAssisted Evolutionary Search In Aerodynamic Shape Design.
In Congress on Evolutionary Computation, Special Session on Design Optimisation with Evolutionary Computation(CEC'03),
Canberra, Australia,
pages 18561863,
2003.
[bibtexkey = Ong03]

H. Ulmer,
F. Streicher,
and A. Zell.
Modelassisted steadystate evolution strategies.
In Proceedings of Genetic and Evolutionary Computation Conference,
LNCS 2723,
pages 610621,
2003.
[bibtexkey = Ulmer03a]

H. Ulmer,
F. Streichert,
and A. Zell.
Evolution startegies assisted by Gaussian processes with improved preselection criterion.
In Proceedings of IEEE Congress on Evolutionary Computation,
pages 692699,
2003.
[bibtexkey = Ulmer03b]

L. Willmes,
T. Baeck,
Y. Jin,
and B. Sendhoff.
Comparing neural networks and kriging for fitness approximation in evolutionary optimization.
In Proceedings of IEEE Congress on Evolutionary Computation,
pages 663670,
2003.
[bibtexkey = Willmes03]

K. Won,
T. Ray,
and K. Tai.
A framework for optimization using approximate functions.
In Proceedings of IEEE Congress on Evolutionary Computation,
pages 10771084,
2003.
[bibtexkey = Won03]

J. Ziegler and W. Banzhaf.
Decreasing the number of evaluations in evolutionary algorithms by using a metamodel of the fitness function.
In C. Ryan,
T. Soule,
M. Keijzer,
E. Tsang,
R. Poli,
and E. Costa, editors,
Proc. Sixth European Conf. Genetic Programming (EuroGP'03),
volume 2610 of Lecture Notes in Computer Science,
Berlin,
pages 264275,
2003.
Springer.
[bibtexkey = ZiB03]

Y. Jin,
M. Olhofer,
and B. Sendhoff.
A framework for evolutionary optimization with approximate fitness functions.
IEEE Transactions on Evolutionary Computation,
6(5):481494,
2002.
[bibtexkey = Jin02a]

L. Albert and D.E. Goldberg.
Efficient disretization scheduling in multiple dimensions.
In Proceedings of Genetic and Evolutionary Computation Conference,
pages 271278,
2002.
Morgan Kaufmann.
[bibtexkey = Albert02]

J.H. Chen,
D.E. Goldberg,
S.Y. Ho,
and K. Sastry.
Fitness inheritance in multiobjective optimization.
In Proceedings of genetic and Evolutionary Computation Conference,
pages 319326,
2002.
Morgan Kaufmann.
[bibtexkey = Chen02]

M. Emmerich,
A. Giotis,
M. Özdenir,
T. Bäck,
and K. Giannakoglou.
Metamodelassisted evolution strategies.
In Parallel Problem Solving from Nature,
number 2439 of Lecture Notes in Computer Science,
pages 371380,
2002.
Springer.
[bibtexkey = Emmer02]

M. Farina.
A neural network based generalized response surface multiobjective evolutionary algorithms.
In Congress on Evolutionary Computation,
pages 956961,
2002.
IEEE Press.
[bibtexkey = Farina02]

M. Hüsken,
Y. Jin,
and B. Sendhoff.
Structure optimization of neural networks for evolutionary design optimization.
In 2002 GECCO Workshop on Approximation and Learning in Evolutionary Computation,
pages 1316,
2002.
[bibtexkey = Huesken02]

Y. Jin and B. Sendhoff.
Fitness approximation in evolutionary computation: A survey.
In Proceedings of the Genetic and Evolutionary Computation Conference,
pages 11051112,
2002.
Morgan Kaufmann.
[bibtexkey = Jin02]

P. Nain and K. Deb.
A computationally effective multiobjective search and optimization techniques using coarsetofine grain modeling.
In 2002 PPSN Workshop on Evolutionary Multiobjective Optimization,
2002.
[bibtexkey = Nain02]

V. Oduguwa and R. Roy.
Multiobjective optimization of rolling rod product design using metamodeling approach.
In Genetic and Evolutionary Computation Conference,
New York,
pages 11641171,
2002.
Morgan Kaufmann.
[bibtexkey = Odu02]

Y. S. Ong,
A.J. Keane,
and P.B. Nair.
SurrogateAssisted Coevolutionary Search.
In 9th International Conference on Neural Information Processing, Special Session on Trends in Global Optimization,
Singapore,
pages 21952199,
2002.
[bibtexkey = Ong02]

K. Rasheed,
S. Vattam,
and X. Ni.
Comparison of methods for using reduced models to speed up design optimization.
In Proceedings of Genetic and Evolutionary Computation Conference,
New York,
pages 11801187,
2002.
Morgan Kaufmann.
[bibtexkey = Rasheed02]

K. Sastry and D. Goldberg.
Genetic algorithms, efficiency enhancement, and deciding well with differing fitness bias values.
In Genetic and Evolutionary Computation Conference,
pages 536543,
2002.
[bibtexkey = SaGo02b]

K. Sastry and D. Goldberg.
Genetic algorithms, efficiency enhancement, and deciding well with differing fitness bias variances.
In Genetic and Evolutionary Computation Conference,
pages 536543,
2002.
[bibtexkey = SaGo02a]

A. Schmitz,
E. Besnard,
and E. Vivies.
Reducing the cost of computational fluid dynamics optimization using multilayer perceptrons.
In IEEE 2002 World Congress on Computational Intelligence,
2002.
IEEE.
[bibtexkey = Schmitz02]

K. Abboud M. Schoenauer.
Surrogate deterministic mutation.
In Artificial Evolution'01,
pages 103115,
2002.
Springer.
[bibtexkey = Abboud02]

M. Farina.
A minimal cost hybrid strategy for Pareto optimal front approximation.
Evolutionary Optimization,
3(1):4152,
2001.
[bibtexkey = Farina01]

J. Branke,
C. Schmidt,
and H. Schmeck.
Efficient fitness estimation in noisy environment.
In L. Spector et al, editor,
Proceedings of Genetic and Evolutionary Computation,
San Francisco, CA,
pages 243250,
July 2001.
Morgan Kaufmann.
[bibtexkey = Branke01]

M.A. ElBeltagy and A.J. Keane.
Evolutionary optimization for computationally expensive problems using Gaussian processes.
In Proceedings of International Conference on Artificial Intelligence,
pages 708714,
2001.
CSREA.
[bibtexkey = Beltagy01]

A. Giotis,
M. Emmerich,
B. Naujoks,
and K. Giannakoglou.
Low kost stochastic optimization for engineering applications.
In Proceedings of International Conference on Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems,
2001.
[bibtexkey = Giotis01]

Y. Jin,
M. Olhofer,
and B. Sendhoff.
Managing approximate models in evolutionary aerodynamic design optimization.
In Proceedings of IEEE Congress on Evolutionary Computation,
volume 1,
pages 592599,
May 2001.
[bibtexkey = Jin01]

H.S. Kim and S.B. Cho.
An efficient genetic algorithms with less fitness evaluation by clustering.
In Proceedings of IEEE Congress on Evolutionary Computation,
pages 887894,
2001.
IEEE.
[bibtexkey = Kim01]

K. Sastry,
D.E. Goldberg,
and M. Pelikan.
Don't evaluate, inherit.
In Proceedings of Genetic and Evolutionary Computation Conference,
pages 551558,
2001.
Morgan Kaufmann.
[bibtexkey = Sastry01]

K.H. Liang,
X. Yao,
and C. Newton.
Evolutionary Search of Approximated NDimensional Landscape.
International Journal of Knowledgebased Intelligent Engineering Systems,
4(3):172183,
2000.
[bibtexkey = Liang00]

G. Schneider.
Neural networks are useful tools for drug design.
Neural Networks,
13:1516,
2000.
[bibtexkey = Schneider00]

M. Hüsken and B. Sendhoff.
Evolutionary optimization for problem classes with Lamarckian inheritance.
In IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks,
pages 98109,
2000.
[bibtexkey = Huesken00]

Y. Jin,
M. Olhofer,
and B. Sendhoff.
On evolutionary optimization with approximate fitness functions.
In Proceedings of the Genetic and Evolutionary Computation Conference,
pages 786792,
2000.
Morgan Kaufmann.
[bibtexkey = Jin00]

K. Rasheed.
Informed operators: Speeding up geneticalgorithmbased design optimization using reduced models.
In Proceedings of genetic and Evolutionary Computation Conference,
Las Vegas,
pages 628635,
2000.
Morgan Kaufmann.
[bibtexkey = Rasheed00]

Y. Sano and H. Kita.
Optimization of noisy fitness functions by means of genetic algorithms using history.
In M. Schoenauer et al, editor,
Parallel Problem Solving from Nature,
volume 1917 of Lecture Notes in Computer Science,
2000.
Springer.
[bibtexkey = Sano00]

M. Sefrioui and J. Periaux.
A hierarchical genetic algorithm using multiple models for optimization.
In Parallel Problem Solving from Nature,
volume 1917 of Lecture Notes in Computer Science,
pages 879888,
2000.
Springer.
[bibtexkey = Sefrioui00]

R. Jin,
W. Chen,
and T.W. Simpson.
Comparative studies of metamodeling techniques under miltiple modeling criteria.
Technical report 20004801,
AIAA,
2000.
[bibtexkey = JinR00]

L. Bull.
On modelbased evolutionary computation.
Soft Computing,
3:7682,
1999.
[bibtexkey = Bull99]

M. Papadrakakis,
N. Lagaros,
and Y. Tsompanakis.
Optimization of largescale 3D trusses using Evolution Strategies and Neural Networks.
Int. J. Space Structures,
14(3):211223,
1999.
[bibtexkey = Papa99]

S. Pierret.
Turbomachinery blade design using a NavierStokes solver and artificial neural network.
ASME Journal of Turbomachinery,
121(3):326332,
1999.
[bibtexkey = Pierret99]

K. Anderson and Y. Hsu.
Genetic crossover strategy using an approximation concept.
In IEEE Congress on Evolutionary Computation,
Washington D.C.,
pages 527533,
1999.
IEEE.
[bibtexkey = Anderson99]

M.A. ElBeltagy,
P.B. Nair,
and A.J. Keane.
Metamodeling techniques for evolutionary optimization of computationally expensive problems: promises and limitations.
In Proceedings of Genetic and Evolutionary Conference,
Orlando,
pages 196203,
1999.
Morgan Kaufmann.
[bibtexkey = Beltagy99]

K.H. Liang,
X. Yao,
and C. Newton.
Combining landscape approximation and local search in global optimization.
In 1999 Congress on Evolutionary Computation,
pages 15141520,
1999.
[bibtexkey = Liang99]

A. Ratle.
Optimal sampling strategies for learning a fitness model.
In Proceedings of 1999 Congress on Evolutionary Computation,
volume 3,
Washington D.C.,
pages 20782085,
July 1999.
[bibtexkey = Ratle99]

W. Shyy,
P. K. Tucker,
and R. Vaidyanathan.
Response surface and neural network techniques for rocket engine injector optimization.
Technical report 992455,
AIAA,
1999.
[bibtexkey = Shyy99]

A. J. Brooker,
J. Dennis,
P. D. Frank,
D. B. Serafini,
V. Torczon,
and M. Trosset.
A rigorous framework for optimization of expensive functions by surrogates.
Structural Optimization,
17:113,
1998.
[bibtexkey = Brooker98]

R.T. Haftka,
E.P. Scott,
and J.R. Cruz.
Optimization and experiments: A survey.
Applied Mechanics Review,
51(7):435448,
1998.
[bibtexkey = Haftka98]

G. Schneider,
W. Schrödl,
and G. Wallukat.
Peptide design by artificial neural networks and computaerbased evolutionary search.
Proceedings of National Academy of Science,
95:1219712184,
1998.
[bibtexkey = Schneider98]

J. Branke.
Creating robust solutions by means of evolutionary algorithms.
In Proceedings of Parallel Problem Solving from Nature,
Lecture Notes in Computer Science,
pages 119128,
1998.
Springer.
[bibtexkey = Branke98]

D. Eby,
R. Averill,
W. Punch,
and E. Goodman.
Evaluation of injection island model GA performance on flywheel design optimization.
In Third Conference on Adaptive Computing in Design and manufacturing,
pages 121136,
1998.
Springer.
[bibtexkey = Eby98]

M.A. ElBeltagy and A.J. Keane.
Optimization for multilevel problems: A comparison of various algorithms.
In I. Parmee, editor,
Proceedings of Third International Conference on Adaptive Computing in Design and Manufacture,
pages 111120,
1998.
Springer.
[bibtexkey = Beltagy98]

B. Johanson and R. Poli.
GPMusic: An interactice genetic programming system for music generation with automated fitness raters.
In John R. Koza,
Wolfgang Banzhaf,
Kumar Chellapilla,
Kalyanmoy Deb,
Marco Dorigo,
David B. Fogel,
Max H. Garzon,
David E. Goldberg,
Hitoshi Iba,
and Rick Riolo, editors,
Proceedings of the Third Annual Conference on Genetic Programming,
pages 181186,
1998.
[bibtexkey = Johanson98]

P.B. Nair and A.J. Keane.
Combining approximation concepts with algorithmbased structural optimization procedures.
In Proceedings of 39th AIAA/ASMEASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference,
pages 17411751,
1998.
[bibtexkey = Nair98]

A. Ratle.
Accelerating the convergence of evolutionary algorithms by fitness landscape approximation.
In A. Eiben,
Th. Bäck,
M. Schoenauer,
and H.P. Schwefel, editors,
Parallel Problem Solving from Nature,
volume V,
pages 8796,
1998.
[bibtexkey = Ratle98]

G. M. Robinson and A. J. Keane.
A Case for MultiLevel Optimisation in Aeronautical Design.
In Proceedings of the RAeS Conf. on Multidisciplinary Design and Optimisation,
pages pp. 9.19.6,
1998.
The Royal Aeronautical Society.
[bibtexkey = Robinson98]

A.A. Giunta and L. Watson.
A comparison of approximation modeling techniques: Polynomial versus interpolating models.
Technical report 984758,
AIAA,
1998.
[bibtexkey = Giunta98]

T. Simpson,
T. Mauery,
J. Korte,
and F. Mistree.
Comparison of response surface and Kriging models for multidiscilinary design optimization.
Technical report 984755,
AIAA,
1998.
[bibtexkey = Simpson98]

J. Dennis and V. Torczon.
Managing approximate models in optimization.
In N. Alexandrov and M. Hussani, editors,Multidisciplinary design optimization: Stateoftheart,
pages 330347.
SIAM,
1997.
[bibtexkey = Dennis97]

T. Morimoto,
J. De Baerdemaeker,
and Y. Hashimoto.
An intelligent approach for optimal control of fruitstorage process using neural networks and genetic algorithms.
Computers and Electronics in Agriculture,
18:205224,
1997.
[bibtexkey = Morimoto97]

A. Neumaier.
Molecular modeling of proteins and mathematical prediction of protein structures.
SIAM Review,
39(3):407460,
1997.
[bibtexkey = Neumaier97]

M. Papadrakakis,
N. Lagaros,
and Y. Tsompanakis.
Structural optimization using evolution strategies and neural networks.
In Fourth U.S. National Congress on Computational Mechanics,
San Francisco,
1997.
[bibtexkey = Papa97]

A. Piccolboni and G. Mauri.
Application of evolutionary algorithms to protein folding prediction.
In J.K. Hao et al, editor,
Proceedings of the Artificial Evolution 97,
volume 1363 of Lecture Notes in Computer Science,
pages 123136,
1997.
Springer.
[bibtexkey = Piccolboni97]

X. Zhang,
B. Julstrom,
and W. Cheng.
Design of vector quantization codebooks using a genetic algorithm.
In Proceedings of the IEEE Conference on Evolutionary Computation,
pages 525529,
1997.
IEEE.
[bibtexkey = Zheng97]

J. Lee and P. Hajela.
Parallel genetic algorithms implementation for multidisciplinary rotor blade design.
Journal of Aircraft,
33(5):962969,
1996.
[bibtexkey = Lee96]

J. Redmond and G. Parker.
Actuator placement based on reachable set optimization for expected disturbance.
Journal Optimization Theory and Applications,
90(2):279300,
August 1996.
[bibtexkey = Redmond96]

H. Vekeria and I. Parmee.
The use of a cooperative multilevel CHC GA for structuralshape optimization.
In Proceedings of fourth European Congress on Intelligent Techniques and Soft Computing,
volume I,
Aachen,
pages 471475,
1996.
[bibtexkey = Vekeria96]

H.P. Schwefel.
Evolution and Optimum Seeking.
Wiley,
1995.
[bibtexkey = Schwefel95]

R. Smith,
B. Dike,
and S. Stegmann.
Fitness inheritance in genetic algorithms.
In Proceedings of ACM Symposiums on Applied Computing,
pages 345350,
1995.
ACM.
[bibtexkey = Smith95]

D. Yang and S.J. Flockton.
Evolutionary algorithms with a coarsetofine function smoothing.
In IEEE International Conference on Evolutionary Computation,
Perth, Australia,
pages 657662,
1995.
IEEE Press.
[bibtexkey = Yang95]

W. Carpenter and J.F. Barthelemy.
Common misconceptions about neural networks as approximators.
ASCE Journal of Computing in Civil Engineering,
8(3):345358,
1994.
[bibtexkey = Carpenter94]

G. Schneider,
J. Schuchhardt,
and P. Wrede.
Artificial neural networks and simulated molecular evolution are potential tools for sequenceoriented protein design.
CABIOS,
10(6):635645,
1994.
[bibtexkey = Schneider94]

J. A. Biles.
GenJam: A genetic algorithm for generating jazz solos.
In Proceedings of International Computer Music Conference,
pages 131137,
1994.
[bibtexkey = Biles94]

D.E. Grierson and W.H. Pak.
Optimal sizing, geometrical and topological design using a genetic algorithm.
Structural Optimization,
6(3):151159,
1993.
[bibtexkey = Grierson93]

S. Tong and B. Gregory.
Turbine preliminary design using artificial intelligence and numerical optimization techniques.
Journal of Turbomachinar,
114(1),
1992.
[bibtexkey = Tong92]

W. Carpenter and J.F. Barthelemy.
A comparison of polynomial approximation and artificial neural nets as response surface.
Technical report 922247,
AIAA,
1992.
[bibtexkey = Carpenter92]

J.M. Fitzpatrick and J.J. Grefenstette.
Genetic algorithms in noisy environments.
Machine Learning,
3:101120,
1988.
[bibtexkey = Fitzpatrick88]

J.J. Grefenstette and J.M. Fitzpatrick.
Genetic search with approximate fitness evaluations.
In Proceedings of the International Conference on Genetic Algorithms and Their Applications,
pages 112120,
1985.
[bibtexkey = Grefenstette85]
BACK TO INDEX
Disclaimer:
This material is presented to ensure timely dissemination of
scholarly and technical work. Copyright and all rights therein
are retained by authors or by other copyright holders.
All person copying this information are expected to adhere to
the terms and constraints invoked by each author's copyright.
In most cases, these works may not be reposted
without the explicit permission of the copyright holder.
Les documents contenus dans ces répertoires sont rendus disponibles
par les auteurs qui y ont contribué en vue d'assurer la diffusion
à temps de travaux savants et techniques sur une base noncommerciale.
Les droits de copie et autres droits sont gardés par les auteurs
et par les détenteurs du copyright, en dépit du fait qu'ils présentent
ici leurs travaux sous forme électronique. Les personnes copiant ces
informations doivent adhérer aux termes et contraintes couverts par
le copyright de chaque auteur. Ces travaux ne peuvent pas être
rendus disponibles ailleurs sans la permission explicite du détenteur
du copyright.
Last modified: Tue Jul 12 14:22:18 2005
Author: yaochu.
This document was translated from BibT_{E}X by
bibtex2html