Abstract
This paper proposes a combination genetic algorithm (GA) for solving the combination optimization problems which can not be naturally solved by standard GAs. A combination encoding scheme and genetic operators are designed for solving combination optimization problems. We apply this combination GA to the portfolio optimization problem which can be reformulated approximately as a combination optimization problem. Experimental results show that the proposed combination GA is effective in solving the portfolio optimization problem.
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References
Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. University of Michigan Press (1975)
Bäck, T., Fogel, D.B., Michalewicz, T. (eds.): Evolutionary Computation 1: Basic Algorithms and Operators. IOS Press, Amsterdam (2000)
Bäck, T., Fogel, D.B., Michalewicz, T. (eds.): Evolutionary Computation 2: Advanced Algorithms and Operators. IOS Press, Amsterdam (2000)
Goldberg, D.E.: Genetic Algorithm in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)
Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1996)
Srinivas, M., Patnaik, L.: Genetic algorithms: A survey. Computer 27, 17–26 (1994)
Davis, L.: Applying adaptive algorithms to epistatic domains. In: Proceedings of the International Joint Conference on Artificial Intelligence, pp. 162–164 (1985)
Goldberg, D., Lingle, R.: Alleles, loci, and the traveling salesman problem. In: Proceedings of the First International Conference on Genetic Algorithms and Their Applications, pp. 154–159 (1985)
Oliver, I., Smith, D., Holland, J.: A study of permutation crossover operators on the traveling salesman problem. In: Proceedings of the Second International Conference on Genetic Algorithms and Their Applications, pp. 224–230 (1987)
Syswerda, G.: Schedule optimization using genetic algorithms. In: Davis, L. (ed.) Handbook of Genetic Algorithms, pp. 332–349. Van Nostrand Reinhold (1991)
Markowitz, H.M., Arnott, R.D.: Portfolio selection. Journal of Finance 7, 77–91 (1952)
Xia, Y., Liu, B., Wang, S., Lai, K.K.: A model for portfolio selection with order of expected returns. Computers and Operations Research, 409–422 (2000)
Kimoto, T., Asakawa, K., Yoda, M., Takeoka, M.: Stock market prediction system with modular neural networks. In: Proceedings of the 1990 International Joint Conference on Neural Networks, pp. 1–6 (1990)
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Chen, JS., Hou, JL. (2006). A Combination Genetic Algorithm with Applications on Portfolio Optimization. In: Ali, M., Dapoigny, R. (eds) Advances in Applied Artificial Intelligence. IEA/AIE 2006. Lecture Notes in Computer Science(), vol 4031. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11779568_23
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DOI: https://doi.org/10.1007/11779568_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35453-6
Online ISBN: 978-3-540-35454-3
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