 Other References: |
Eklund, Neil Holger White. Multiobjective visible spectrum optimization: A genetic algorithm approach. Proquest Dissertations And Theses 2002. Section 0185, Part 0984 167 pages; [Ph.D. dissertation].United States—New York: Rensselaer Polytechnic Institute; 2002. Publication No. AAT 3072196.
Eklund, Neil Holger White. Multiobjective Visible Spectrum Optimization: A Genetic Algorithm Approach. Proquest Dissertations And Theses 2002. Section 0185, Part 0984 167 pages; [Ph.D. dissertation]. United States—New York: Rensselaer Polytechnic Institute; 2002. Publication No. AAT 3072196.
JPMorgan/Reuters RiskMetrics™—Technical Documents Fourth Edition, Dec. 17, 1996.
Jorge Mina & Andrew Ulmer Delta-Gamma Four Ways Aug. 31, 1999.
Stefan Pichler & Karl Selitsch A Comparison of Analytical VaR Methodologies for Portfolios That Include Options Dec. 1999.
Jorge Mina & Jerry Yi Xiao return to RiskMetrics: The Evolution of a Standard Apr. 2001.
Abbass, H.A., Sarker, R Newton, C, PDE:a Pareto-frontier differential evolution approach for multi-ojective optimization problems, Proceedings of the 2001 Congress on Evolutionary Computation, vol. 2, IEEE, May 27-30, 2001.
Ignizio, James, An Algorithm for Solving the Linear Goal Programming Problem by Solving Its Dual, The Journal of the Operational research Society, vol. 36, No. 6 (Jun. 1985) pp. 507-515.
Masud, Abu, Interactive Sequential Goal Programming, The Journal of the Operational Research Society, vol. 32, No. 5 (May 1981), pp. 391-400.
Bellmore, M, Generalized Penalty function Concepts in Mathmatical Optimization, Operations research, vol. 18, No. 2 (Mar.-Apr. 1970), pp. 229-252.
Iwamura, K., Chances constrained integer Programming Models for Capital Budgeting in Fizzy Environments, The Journal of the Operational Research Society, vol. 49, No. 8 (Aug. 1998), pp. 854-860.
Iyer, Naresh Sundaram, “A family of dominance filters for multiple criteria decision making: Chossing the right filter for a decision situation”. Proquest Dissertations and Theses 2001, Section 0168, Part 0984 169 pages: [Ph.D. dissertation]. United States—Ohio: The Ohio State University: 2001, Publication No. AAT 3035522.
Albanese, C., et al. Dimension Reduction in the computation of Value-at-risk, Feb. 28, 2002.
Baglioni, S., et al., An Evolutionary Approach to Multiperiod Asset Allocation, Genetic Programming, (EuroGP'2000), vol. 1802, pp. 225-236, Springer-Verlag, Edinburgh, 2000.
Loraschi, A., et al., An Evolutionary Algorithm For Portfolio Selection In A Downside Risk Framework, European Journal of Finance, 1994.
Kalvelagen, E., Solving Multi-Objective Models With GAMS, citeseer.nj.nec.com/article/kalvelagen02solving.html, Sep. 20, 2002.
Freedman, Roy S., A Comparison of Stochastic Search Heuristics for Portfolio Optimization, Second International Conference on Artificial Intelligence Applications on Wall Street, Software Engineering Press, Apr. 1993, pp. 149-151.
Streichert, F., Introduction to Evolutionary Algorithms, University of Tuebingen, Presented at the Frankfurt MathFinance Workshop, Apr. 2, 2002.
Korczak, et al., Evolutionary Approach to Portfolio Optimization, University of Wroclaw, Institute of Computer Science, 2001.
Eklund, N., Multi-objective optimization of spectra using genetic algorithms, Dept. of Decision Sciences and Engineering Systems, RPI, 2001.
Bodie, Z., et al., Investments, Fourth edition, Irwin/McGraw Hill, 1999.
DeFusco, R.A., et al., Quantitative Methods for Investment Analysis, AIMR, 2001.
Hull, J.C., Options, Futures & Other Derivatives, Fourth Edition, Prentice-Hall, 2000.
Back, Thomas, Evolutionary Algorithms in Theory and Practice, Oxford University Press, New York, 1996.
Breiman, L., et al., Classification and Regression Trees, Wadsworth International Group, California, 1984.
De ath, G., Multivariate Regression Trees: A new technique for modeling species-environment relationships, Ecology, pp. 1105, 2001.
Coello, C., et al. Evolutionary Algorithm MOP Approaches, Evolutionary Algorithms for Solving Multi-Objective Problems, pp. 59-99, Kluwer Academic, 2002.
Goldberg, D., Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Massachusetts, 1989.
Dempster, A., Upper and lower probabilities induced by a multivalued mapping, Annals of Mathematical Statistics, 38: 325-339, 1976.
Shafer, G., A Mathematical Theory of Evidence, Princeton University Press, Princeton, New Jersey, 1967.
Schweizer B., et al., Associative Functions and Abstract Semi-Groups, Publicationes Mathematicae Debrecen, 10:69-81, 1963.
Bonissone, P., Summarizing and Propagating Uncertain Information with Triangular Norms, International Journal of Approximate Reasoning, 1(1):71-101, Jan. 1987.
Winston, W., Operations Research: Applications and Algorithms, Duxbury Press, Belmont, California, 1994.
Goldberg, D., The Design of Innovation: Lessons from and for Competent Genetic Algorithms Kluwer Academic Publishers, Norwell, Mass., 2002.
Michalewicz, Z., Genetic Algorithms+Data Structures=Evolution Programs (3rd. Ed). Springer-Verlag, Berlin, 1996.
Josephson, J.R., et al. An Architecture for Exploring Large Design Spaces, National Conference of the American Association for Artificial Intelligence, Madison, Wis., pp. 143-150, 1998.
Tarascio, V., Pareto's Methodological Approach to Economics, University of North Carolina Press, Chapel Hill, Va., 1968.
Fong, G., A Multidimensional Framework for Risk Analysis, Financial Analysts Journal, Jul./Aug. 1997.
Larsen, D., et al., Multivariate Regression Tree for analysis of abundance data, 2002.
Reilly, F.K., et al., Investment Analysis and Portfolio Management, 6th Edition, Dryden, 2000.
Fabozzi, F.J., Fixed income analysis for the chartered financial analyst program, Fabozzi Associates, 2000.
Indraneel, D., et al., A Closer Look at Drawbacks of Minimizing Weighted Sums of Objectives for Pareto Set Generation in Multicriteria Optimization Problems Structural Optimization, 14(1), pp. 63-69, 1997.
Schaffer, J.D., et al., Multi-objective Learning via Genetic Algorithms, Ninth International Joint Conference on Artificial Intelligence, pp. 593-595, 1985.
Subbu, Raj, et al., Modeling and Convergence Analysis of Distributed Coevolutionary Algorithms, IEEE International Congress on Evolutionary Computation, pp. 1276-1283, 2000.
Greene, F., A method for utilizing diploid/dominance in genetic search, First IEEE International Conference on Evolutionary Computation, v1, 439. IEEE Service Center, Piscataway, N.J., pp. 171-176, 1994.
(In book “Foundations of Genetic Algorithms”) D. Goldberg and K. Deb, A comparison of selection schemes used in genetic algorithms. In “Foundations of Genetic Algorithms,” Rawlins, G. (Ed.), 69. M. Kaufmann Publishers, San Mateo, Calif., 69-93, 1991 (select pages from book, including paper Abstract).
Coello, C., et al., MOSES: A Multiobjective optimization tool for engineering design, Engineering Optimization, 31:337-368, 1999.
Horn, J., Multicriterion Decision Making, Handbook of Evolutionary Computation, (Back T, Fogel DB and Michalewicz Z, eds.), pp. F1.9:1-F1.9:15, IOP Publication Ltd and Oxford University Press, 1997.
Subbu, Raj, et al., Modeling and Convergence Analysis of Distributed Coevolutionary Algorithms, IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics, vol. 34, No. 2, Apr. 2004.
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