Trading Rules on Stock Markets Using Genetic Network Programming with Reinforcement Learning and Importance Index
Author:
Affiliation:
1. Graduate School of Information, Production, and Systems, Waseda University
Publisher
Institute of Electrical Engineers of Japan (IEE Japan)
Subject
Electrical and Electronic Engineering
Link
https://www.jstage.jst.go.jp/article/ieejeiss/127/7/127_7_1061/_pdf
Reference12 articles.
1. (1) J. H. Holland: “Adaptation in Natural and Artificial Systems”, University of Michigan Press, 1975, MIT Press (1992)
2. (2) J. R. Koza.: “Genetic Programming, on the programming of computers by means of natural selection”, Cambridge, Mass., MIT Press (1992)
3. (3) S. Mabu, K. Hirasawa, and J. Hu: “A graph-based evolutionary algorithm: Genetic network programming (gnp) and its extension using reinforcement learning”, Evolutionary Computation, MIT Press (to appear)
4. (4) T. Eguchi, K. Hirasawa, J. Hu, and N. Ota: “A Study of Evolutionary Multiagent Models Based on Symbiosis”, IEEE Trans. on Systems, Man and Cybernetics Part B, Vol. 36, No. 1, pp. 179-193 (2006-2)
5. (5) H. Katagiri, K. Hirasawa, J. Hu, and J. Murata: “Genetic Network Programming and its application”, IEEJ Trans. EIS, Vol. 122, No. 12, pp. 2149-2156 (2002-12) (in Japanese)
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