Using support vector machine and dynamic parameter encoding to enhance global optimization

References

• Backlund, P. B., D. W. Shahan, and C. C. Seepersad. 2012. “A Comparative Study of the Scalability of Alternative Metamodelling Techniques.” Engineering Optimization 44: 767–786. doi: 10.1080/0305215X.2011.607817
   Web of Science ®Google Scholar
• Breiman, L. 1996. “Bagging Predictors.” Machine Learning 24: 123–140.
   Web of Science ®Google Scholar
• Bull, L. 1999. “On Model-based Evolutionary Computation.” Soft Computing—A Fusion of Foundations, Methodologies and Applications 3: 76–82.
   Google Scholar
• Chalimourda, A., B. Schölkopf, and A. J. Smola. 2004. “Experimentally Optimal v in Support Vector Regression for Different Noise Models and Parameter Settings.” Neural Networks 17: 127–141. doi: 10.1016/S0893-6080(03)00209-0
   PubMed Web of Science ®Google Scholar
• Chen, X., K. Chen, and K. Huang. 2008. “A Microstrip Grid Array Antenna Optimized by a Parallel Genetic Algorithm.” Microwave and Optical Technology Letters 50: 2976–2978. doi: 10.1002/mop.23856
   Web of Science ®Google Scholar
• Clarke, S. M., J. H. Griebsch, and T. W. Simpson. 2005. “Analysis of Support Vector Regression for Approximation of Complex Engineering Analyses.” Journal of Mechanical Design 127: 1077–1087. doi: 10.1115/1.1897403
   Web of Science ®Google Scholar
• Dorigo, M., and L. M. Gambardella. 1997. “Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem.” IEEE Transactions on Evolutionary Computation 1: 53–66. doi: 10.1109/4235.585892
   Google Scholar
• Eberhart, R. C., and J. Kennedy. 1995. “A New Optimizer Using Particle Swarm Theory.” Proceedings of the Sixth International Symposium on Micro Machine and Human Science 1: 39–43. doi: 10.1109/MHS.1995.494215
   Google Scholar
• El-Beltagy, M. A., and A. J. Keane. 1998. “Optimisation for Multilevel Problems: A Comparison of Various Algorithms.” Adaptive Computing in Design and Manufacture, 111–120. London: Springer.
   Google Scholar
• Emmerich, M., A. Giotis, M. Özdemir, T. Bäck, and K. Giannakoglou. 2002. “Metamodel-assisted Evolution Strategies.” Parallel Problem Solving from Nature—PPSN VII, 361–370. Berlin: Springer.
   Google Scholar
• Fletcher, R. 1987. Practical Methods of Optimization. New York: John Wiley and Sons.
   Google Scholar
• Freund, Y. 1995. “Boosting a Weak Learning Algorithm by Majority.” Information and Computation 121: 256–285. doi: 10.1006/inco.1995.1136
   Web of Science ®Google Scholar
• Goldberger, A. S. 1962. “Best Linear Unbiased Prediction in the Generalized Linear Regression Model.” Journal of the American Statistical Association 57: 369–375. doi: 10.1080/01621459.1962.10480665
   Web of Science ®Google Scholar
• Grierson, D. E., and W. H. Pak. 1993. “Optimal Sizing, Geometrical and Topological Design Using a Genetic Algorithm.” Structural Optimization 6: 151–159. doi: 10.1007/BF01743506
   Web of Science ®Google Scholar
• Holland, J. H. 1975. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. Ann Arbor: University of Michigan Press.
   Google Scholar
• Jin, Y., and J. Branke. 2005. “Evolutionary Optimization in Uncertain Environments—A Survey.” IEEE Transactions on Evolutionary Computation 9: 303–317. doi: 10.1109/TEVC.2005.846356
   Web of Science ®Google Scholar
• Jin, Y., M. Olhofer, and B. Sendhoff. 2002. “A Framework for Evolutionary Optimization with Approximate Fitness Functions.” IEEE Transactions on Evolutionary Computation 6: 481–494. doi: 10.1109/TEVC.2002.800884
   Web of Science ®Google Scholar
• Kerkhoff, A. J., and H. Ling. 2007. “Design of a Band-notched Planar Monopole Antenna Using Genetic Algorithm Optimization.” IEEE Transactions on Antennas and Propagation 55: 604–610. doi: 10.1109/TAP.2007.891563
   Web of Science ®Google Scholar
• Nair, P. B., A. J. Keane, and R. P. Shimpi. 1998. “Combining Approximation Concepts with Genetic Algorithm-based Structural Optimization Procedures.” Proceedings of the 39th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, 1741–1751.
   Google Scholar
• Nakano, H., T. Kawano, Y. Kozono, and J. Yamauchi. 2005. “A Fast MoM Calculation Technique Using Sinusoidal Basis and Testing Functions for a Wire on a Dielectric Substrate and its Application to Meander Loop and Grid Array Antennas.” IEEE Transactions on Antennas and Propagation 53: 3300–3307. doi: 10.1109/TAP.2005.856314
   Web of Science ®Google Scholar
• Plutowski, M., and H. White. 1993. “Selecting Concise Training Sets from Clean Data.” IEEE Transactions on Neural Networks 4: 305–318. doi: 10.1109/72.207618
   PubMed Web of Science ®Google Scholar
• Prakash, S., E. Deelman, and R. Bagrodia. 2000. “Asynchronous Parallel Simulation of Parallel Programs.” IEEE Transactions on Software Engineering 26: 385–400. doi: 10.1109/32.846297
   Web of Science ®Google Scholar
• Ratle, A. 1998. “Accelerating the Convergence of Evolutionary Algorithms by Fitness Landscape Approximation.” Parallel Problem Solving from Nature—PPSN V, 87–96. Berlin: Springer.
   Google Scholar
• Rosenblatt, F. 1961. Principles of Neurodynamics. Perceptrons and the Theory of Brain Mechanisms (No. VG-1196-G-8). Cornell Aeronautical Lab Inc., Buffalo, NY.
   Google Scholar
• Schraudolph, N. N., and R. K. Belew. 1992. “Dynamic Parameter Encoding for Genetic Algorithms.” Machine Learning 9: 9–21.
   Web of Science ®Google Scholar
• Ulmer, H., F. Streichert, and A. Zell. 2003. “Model-assisted Steady-state Evolution Strategies.” Genetic and Evolutionary Computation—GECCO 2003, 610–621. Berlin: Springer.
   Google Scholar
• Vapnik, V. N. 1995. The Nature of Statistical Learning Theory. New York: Springer.
   Google Scholar
• Vrugt, J. A., B. A. Robinson, and J. M. Hyman. 2009. “Self-adaptive Multimethod Search for Global Optimization in Real-parameter Spaces.” IEEE Transactions on Evolutionary Computation 13: 243–259. doi: 10.1109/TEVC.2008.924428
   Web of Science ®Google Scholar
• Zhou, Z., Y. S. Ong, P. B. Nair, A. J. Keane, and K. Y. Lum. 2007. “Combining Global and Local Surrogate Models to Accelerate Evolutionary Optimization.” IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 37: 66–76. doi: 10.1109/TSMCC.2005.855506
   Web of Science ®Google Scholar