System and method for estimation of a distribution algorithm

Data processing: artificial intelligence – Machine learning – Genetic algorithm and genetic programming system

Reexamination Certificate

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

C706S045000

Reexamination Certificate

active

07428514

ABSTRACT:
The underlying invention generally relates to the field of Estimation of Distribution Algorithm, especially to optimization problems, including single-objective optimization and Multi-Objective Optimization.The proposed method for optimization comprises six steps. In a first step it provides an initial population or a data set with a plurality of members respectively represented by parameter sets. Then one or a plurality of fitness functions are applied to evaluate the quality of the members of the population. In a third step offspring of the population is generated by means of a stochastic model using information from all members of the population. One or a plurality of fitness functions are applied to evaluate the quality of the offspring with respect to the underlying problem of the optimization. In a fifth step offspring is selected. Lastly the method goes back to the third step until the quality reaches a threshold value.

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: 6086617 (2000-07-01), Waldon 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: 6748574 (2004-06-01), Sasagawa et al.
patent: 6781682 (2004-08-01), Kasai et al.
patent: 6879388 (2005-04-01), Kasai et al.
patent: 6917882 (2005-07-01), Selifonov 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: 7277893 (2007-10-01), Aggarwal
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: 1205877 (2002-05-01), None
patent: WO 02/45012 (2002-06-01), None
patent: WO 02/057946 (2002-07-01), None
Angeline, Peter J., “Adaptive And Self-Adaptive Evolutionary Computations,” Computational Intelligence: A Dynamic Systems Perspective, Palaniswami et al. (EDS), 1995, pp. 152-163.
Back, T et al., “Evolutionary Computation: Comments on the History and Current State,” IEEE Transactions on Evolutionary Computation, Apr. 1997, pp. 3-17, vol. 1, No. 1.
Back, T. et al., “A Survey of Evolution Strategies,” Proc. of the 4thInt'l Conf. on Genetic Algorithms, Jul. 1991, pp. 2-9.
Carson, Y. et al., “Simulation Optimization: Methods and Applications,” Proc. of the 29thWinter Simulation Conf., 1997, pp. 118-126.
Eiben, A. et al., “Parameter Control In Evolutionary Algorithms,” IEEE Transactions On Evolutionary Computation, vol. 3, No. 2, 1999, pp. 124-141.
European Search Report, EP Application No. 01104723, Aug. 22, 2001, 3 pages.
European Search Report, EP Application 04010194, Jun. 7, 2006, 3 pages.
Fagarasan, F., “A Genetic Algorithm With Variable Length Genotypes. Application In Fuzzy Modeling,” Proceedings Of the Fourth European Congress on Intelligent Techniques, EUFIT '96, vol. 1, Sep. 2-5, 1996, pp. 405-409.
Koumoutsakos, P. et al., “Evolution Strategies for Parameter Optimization in Jet Flow Control,” Center of Turbulence Research Annual Research Briefs, 1998.
Muller, S. et al., “Application of Machine Learning Algorithms to Flow Modeling and Optimization,” Center of Turbulence Research Annual Research Briefs, 1999.
Pittman, J. et al., “Fitting Optimal Piecewise Linear Functions Using Genetic Algorithms,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Jul. 2000, pp. 701-718, vol. 22, Issue 7.
Sbalzarini, I. et al., “Evolutionary Optimization for Flow Experiments,” Center of Turbulence Research Annual Research Briefs, 2000.
Srikanth, R. et al., “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, K. et al., “Discrete NURBS-Surface Approximation Using An Evolutionaary Strategy,” REIHE CI 87/00, SFB 531, 2000, pp. 1-7.
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.
Chen Y. et al., “Feature Subimage Extraction for Cephalogram Landmarking”, Proc. of the 20thAnnual International Conference of the IEEE Engineering in Medicine and Biology Society, Oct. 29, 1998, pp. 1414-1417.
Costa, M. et al., MOPED: A Multi-Objective Parzen-Based Estimation of Distribution Algorithm for Continuous Problems, Polytechnic of Turin, 13 pages, Turin, Italy.
Crump, K.S., “Numerical Inversion of Laplace Transforms Using a Fourier Series Approximation,” Journal of the ACM (JACM), Jan. 1976, vol. 23, No. 1.
Dasgupta, D. et al.., Evolutionary Algorithms In Engineering Applications, Mar. 23, 1997, Springer-Verlag.
Dash, P.K. et al., “Genetic Optimization of a Self Organizing Fuzzy-Neural Network for Load Forecasting,” IEEE Power Engineering Society Winter Meeting, Jan. 23-27, 2000. pp. 1011-1016, vol. 2.
Deb, K., Evolutionary Algorithms for Multi-Criterion Optimization in Engineering Design, In Miettinen et al., Evolutionary Algorithms in Engineering and Computer Science, 1999, pp. 135-161, John Wiley and Sons, Ltd., Chichester, UK.
Eshelman, L, et al., “Crossover Operator Biases: Exploiting the Population Distribution,” Proceedings of the Seventh International Conference on Genetic Algorithms, 1997, pp. 354-361.
Eshelman, L. et al., “Real-Coded Genetic Algorithms and Interval-Schemata,” Philips Laboratories, pp. 187-202, New York, New York, US.
European Search Report, EP Application No. 00124824, Jun. 14, 2001, 3 pages.
European Search Report, EP Application No. 0124825, May 14, 2001, 3 pages.
Fukuda, K., Aug. 26, 2004, What is Voronoi Diagram in Rd?, [online] [Retrieved on Aug. 18, 2005] Retrieved from the Internet<URL:http://www.ifor.math.ethz.ch/˜fukuda/polyfaq
ode29.html>.
Graves, R.W. et al., “Acoustic Wavefield Propagation Using Paraxial Explorators,” ACM, 1988, pp. 1157-1175.
Grierson, D.E. et al., “Optimal Sizing, Geometrical and Topological Design Using a Genetic Algorithm”, Structural Optimization, 1993, pp. 151-159, vol. 6.
Guerin, S. ObjectGarden: Evolving Behavior of Agents via Natural Selection on Weights and Topologies of Neural Networks,: May 1999, pp. 1-15.
Gupta, N. et al., “Automated Test Data Generation Using an Iterative Relaxation Method,” Proceedings of the 6thACM SIGSOFT International Symposium on Foundations of Software Engineering, ACM SIGSOFT Software Engineering Notes, Nov. 1998, vol. 23, No. 6.
Harik, G. et al., “The Compact Genetic Algorithm,” IEEE, 1998, pp. 523-528.
Ishibuchi, H. et al., “Local Search Procedures in A Multi-Objective Genetic Local Search A

LandOfFree

Say what you really think

Search LandOfFree.com for the USA inventors and patents. Rate them and share your experience with other people.

Rating

System and method for estimation of a distribution algorithm does not yet have a rating. At this time, there are no reviews or comments for this patent.

If you have personal experience with System and method for estimation of a distribution algorithm, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and System and method for estimation of a distribution algorithm will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFUS-PAI-O-3980724

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.