Strategy parameter adaptation in evolution strategies

Details Strategy parameter adaptation in evolution strategies Strategy parameter adaptation in evolution strategies

2007-07-10

2007-07-10

Rodriguez, Paul (Department: 2123)

Data processing: structural design, modeling, simulation, and em

Simulating nonelectrical device or system

Mechanical

C703S002000, C703S006000, C706S015000, C706S026000

Reexamination Certificate

active

10080742

ABSTRACT:
The present invention relates to a optimization method based on an evolution strategy according to which a model/structure/shape/design to be optimized is described by parameter sets comprising object parameters. The object parameters are mutated to create offsprings of the parameter set. The quality of the offsprings is evaluated. The parameter set furthermore comprises at least one strategy parameter representing the step-size of the mutation (f.e. the variance of the normal distribution) of associated object parameters. The number of object parameters as well as the number of associated strategy parameters can be adapted during the optimization process. The value of newly inserted strategy parameters can be estimated based on the information of correlated object parameters.

REFERENCES:
patent: 5074752 (1991-12-01), Murphy et al.
patent: 5136686 (1992-08-01), Koza
patent: 5148513 (1992-09-01), Koza et al.
patent: 5265830 (1993-11-01), Allen
patent: 5319781 (1994-06-01), Syswerda
patent: 5355528 (1994-10-01), Roska et al.
patent: 5461570 (1995-10-01), Wang et al.
patent: 5487130 (1996-01-01), Ichimori et al.
patent: 5541848 (1996-07-01), McCormack et al.
patent: 5724258 (1998-03-01), Roffman
patent: 5819244 (1998-10-01), Smith
patent: 5924048 (1999-07-01), McCormack et al.
patent: 6285968 (2001-09-01), Motoyama et al.
patent: 6292763 (2001-09-01), Dunbar et al.
patent: 6430993 (2002-08-01), Seta
patent: 6449603 (2002-09-01), Hunter
patent: 6516309 (2003-02-01), Eberhart et al.
patent: 6549233 (2003-04-01), Martin
patent: 6578018 (2003-06-01), Ulyanov
patent: 6606612 (2003-08-01), Rai et al.
patent: 6654710 (2003-11-01), Keller
patent: 6662167 (2003-12-01), Xiao
patent: 6781682 (2004-08-01), Kasai et al.
patent: 6879388 (2005-04-01), Kasai et al.
patent: 6928434 (2005-08-01), Choi et al.
patent: 6950712 (2005-09-01), Ulyanov et al.
patent: 7043462 (2006-05-01), Jin et al.
patent: 7047169 (2006-05-01), Pelikan et al.
patent: 2002/0099929 (2002-07-01), Jin et al.
patent: 2002/0138457 (2002-09-01), Jin et al.
patent: 2002/0165703 (2002-11-01), Olhofer et al.
patent: 2003/0030637 (2003-02-01), Grinstein et al.
patent: 2003/0055614 (2003-03-01), Pelikan et al.
patent: 2003/0065632 (2003-04-01), Hubey
patent: 2003/0191728 (2003-10-01), Kulkami et al.
patent: 2004/0014041 (2004-01-01), Allan
patent: 2004/0030666 (2004-02-01), Ulyanov et al.
patent: 2004/0034610 (2004-02-01), Marra et al.
patent: 2004/0049472 (2004-03-01), Hayashi et al.
patent: 2005/0209982 (2005-09-01), Jin et al.
patent: 2005/0246297 (2005-11-01), Chen et al.
patent: 1 205 863 (2002-05-01), None
patent: 1 205 877 (2002-05-01), None
patent: 1205877 (2002-05-01), None
patent: WO 02/057946 (2002-07-01), None
Bäck, T. et al. “Evolutionary Computation: Comments on the History and Current State.” IEEE Transactions on Evolutionary Computation, Apr. 1997. vol. 1, No. 1, pp. 3-17.
Carson, Y. et al. “Simulation Optimization: Methods and Applications.” Proc. of the 29th Winter Simulation Conf. 1997. pp. 118-126.
Bäck, T. et al. “A Survey of Evolution Strategies.” Proc. of the 4th Int'l Conf. on Genetic Algorithms. Jul. 1991. pp. 2-9.
Sbalzarini, I. et al. “Evolutionary Optimization for Flow Experiments.” Center of Turbulence Research Annual Research Briefs. 2000.
Müller, S. et al. “Application of Machine Learning Algorithms to Flow Modeling and Optimization.” Center of Turbulence Research Annual Research Briefs. 1999.
Koumoutsakos, P. et al. “Evolution Strategies for Parameter Optimization in Jet Flow Control.” Center of Turbulence Research Annual Research Briefs. 1998.
Pittman, J. et al. “Fitting Optimal Piecewise Linear Functions Using Genetic Algorithms”. IEEE Transactions on Pattern Analysis and Machine Intelligence. Jul. 2000. vol. 22, Issue 7, pp. 701-718.
Olhoferm M., Y. Jin and B. Sendhoff. “Adaptive Encoding for Aerodynamic Shape Optimization Using Evolution Strategies.” Proceedings of IEEE Congress on Evolutionary Computation. May 2001. vol. 1, pp. 576-583.
Jin, Y., M. Olhofer, and B. Sendhoff, “A Framework for Evolutionary Optimization with Approximate Fitness Functions.” IEEE Transactions on Evolutionary Computation. 2002. vol. 6, Ed. 5, pp. 481-494.
Nashvili, M., M. Olhofer. and B. Sendhoff, “Morphing Methods in Evolutionary Design Optimization.” Genetic and Evolutionary Computation Conference (GECCO '05). Jun. 25-29, 2005. pp. 897-904.
Hasenjäger, M., T. Sonoda, B. Sendhoff, and T. Arima. “Three Dimensional Evolutionary Aerodynamic Design Optimization with CMA-ES.” Genetic and Evolutionary Computation Conference (GECCO '05). Jun. 25-29, 2005. pp. 2173-2180.
Angeline, Peter J., “Adaptive And Self-Adaptive Evolutionary Computations,” Computational Intelligence: A Dynamic Systems Perspective, Palaniswami et al. (EDS), 1995, pp. 152-163.
Elben, Agoston E., Hinterding, Robert, and Michalewicz, Zbigniew, “Parameter Control in Evolutionary Algorithms,” IEEE Transactions On Evolutionary Computation, vol. 3, No. 2, 1999, pp. 124-141.
Fagarasan, Florin, Negoita, Dr. Mircea Gh., “A Genetic Algorithm With Variable Length Genotypes. Applications In Fuzzy Modeling,” Proceedings Of the Fourth European Congress on Intelligent Techniques, EUFIT '96, vol. 1, 2-5, Sep. 1996, pp. 405-409.
Srikanth, R., George, R., and Warsi, N., “A Variable-Length Genetic Algorithm For Clustering And Classification,” Pattern Recognition Letters, North-Holland Publ. Amsterdam, NL, vol. 16, No. 8, Aug. 1, 1995, pp. 789-800.
Weinert, Klaus, and Mehnen, Jörn, “Discrete NURBS-Surface Approximation Using An Evolutionary Strategy,” REIHE CI 87/00, SFB 531, 2000, pp. 1-7.
European Search Report, European Application No. 01104723, Aug. 22, 2001, 3 pages.
Hruschka, E.R. et al., “Using a Clustering Genetic Algorithm for Rule Extraction from Artificial Neural Networks,” IEEE, 2000, pp. 199-206.
Kim, H.S. et al., “An Efficient Genetic Algorithm with Less Fitness Evaluation by Clustering,” IEEE, 2001, pp. 887-894.
Li, M. et al, “Hybrid Evolutionary Search Method Based on Clusters,” IEEE Transaction on Pattern Analysis and Machine Intelligence, Aug. 2001, pp. 786-799, vol. 23, No. 8.
Liu, F. et al., “Designing Neural Networks Ensembles Based on the Evolutionary Programming,” Proceedings of the Second International Conference on Machine Learning and Cybernetics, Xi'an, IEEE, Nov. 2-5, 2003, pp. 1463-1466.
Zhou, Z.H. et al., “Genetic Algorithm Based Selective Neural Networks Ensemble,” Proceedings of the 17thInternational Joint Conference on Artificial Intelligence, IEEE, 2001, pp. 797-802, vol. 2.
Agrez, D. “Active Power Estimation by Averaging of the DFT Coefficients,” Proceedings of the 17thIEEE.
Instrumentation and Measurement Technology Conference, May 1-4, 2000, pp. 630-635, vol. 2.
Baluja, S. et al., “Combining Multiple Optimization Runs With Optimal Dependency Trees,” Jun. 30, 1997, 12 pages, CMU-CS-97-157, Justsystem Pittsburgh Research Center, Pittsburgh, PA and School Of Computer Science, Carnegie Mellon University, Pittsburgh, PA.
Baluja, S., “Population-Based Incremental Learning: A Method For Integrating Genetic Search Based Function Optimization And Competitive Learning,” Population Based Incremental Learning, Jun. 2, 1994, pp. 1-41, CMU-CS-94-163, School Of Computer Science, Carnegie Mellon University, Pittsburgh, PA.
Bosman, P. et al., “An Algorithmic Framework For Density Estimation Based Evolutionary Algorithms,” Dec. 1999, pp. 1-63, Department Of Computer Science, Utrecht University, Utrecht, The Netherlands.
Bosman, P. et al., “IDEAs Based On The Normal Kernels Probability Density Function,” Mar. 2000, pp. 1-16, Department Of Computer Science, Utrecht University, Utrecht, The Netherlands.
Bosman, P. et al., “Mixed IDEAs,” Dec. 2000, pp. 1-71, UU-CS

Affiliated with

Also associated with

Rating

Strategy parameter adaptation in evolution strategies does not yet have a rating. At this time, there are no reviews or comments for this patent.

If you have personal experience with Strategy parameter adaptation in evolution strategies, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Strategy parameter adaptation in evolution strategies will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFUS-PAI-O-3794596