nach oben

Erschienen in:

2018 | OriginalPaper | Buchkapitel

Automated Design of Genetic Programming Classification Algorithms for Financial Forecasting Using Evolutionary Algorithms

verfasst von : Thambo Nyathi, Nelishia Pillay

Erschienen in: Theory and Practice of Natural Computing

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

In this work two metaheuristic algorithms namely, a genetic algorithm (GA) and grammatical evolution (GE) are used to configure genetic programming classification algorithms for financial forecasting. The performance of the classifiers evolved through a GA and GE design are compared to the performance of classifiers evolved using the traditional manual design approach. Fifteen stocks from varied sectors are selected to evaluate the performance. Additionally, the fitness landscape of the design space evolved by grammatical evolution and the genetic algorithm is evaluated. Results demonstrate that GE designed algorithms evolve classifiers that perform better than those designed by a GA and manually designed. Furthermore, it is established that the GA design space is more rugged than the GE design space.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Fuel Efficient Truck Platooning with Time Restrictions and Multiple Speeds Solved by a Particle Swarm Optimisation

Nächstes Kapitel Optimizing Fleet Staging of Air Ambulances in the Province of Ontario

Atsalakis, G.S., Valavanis, K.P.: Surveying stock market forecasting techniques-part II: Soft computing methods. Expert. Syst. Appl. 36(3), 5932–5941 (2009)CrossRef

Chen, S.H., Yeh, C.H., Lee, W.C.: Option pricing with genetic programming. In: Genetic Programming 1998: Proceedings of the Third, pp. 32–37. Morgan Kaufmann (1998)

Dobslaw, F.: A parameter tuning framework for metaheuristics based on design of experiments and artificial neural networks. In: International Conference on Computer Mathematics and Natural Computing. WASET (2010)

Eiben, A.E., Michalewicz, Z., Schoenauer, M., Smith, J.E.: Parameter control in evolutionary algorithms. In: Parameter setting in evolutionary algorithms, pp. 19–46. Springer (2007)

Espejo, P.G., Ventura, S., Herrera, F.: A survey on the application of genetic programming to classification. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 40(2), 121–144 (2010)

Fernández-Blanco, P., Bodas-Sagi, D.J., Soltero, F.J., Hidalgo, J.I.: Technical market indicators optimization using evolutionary algorithms. In: Proceedings of the 10th Annual Conference Companion on Genetic and Evolutionary Computation, pp. 1851–1858. ACM (2008)

Goldberg, D.E., Holland, J.H.: Genetic algorithms and machine learning. Mach. Learn. 3(2), 95–99 (1988)CrossRef

Haraldsson, S.O., Woodward, J.R.: Automated design of algorithms and genetic improvement: contrast and commonalities. In: Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation, pp. 1373–1380. ACM (2014)

Hutter, F.: Automated configuration of algorithms for solving hard computational problems. Ph.D. thesis, University of British Columbia (2009)

10.

Hutter, F., Hoos, H.H., Stützle, T.: Automatic algorithm configuration based on local search. In: AAAI, vol. 7, pp. 1152–1157 (2007)

11.

Iba, H., Sasaki, T.: Using genetic programming to predict financial data. In: Proceedings of the 1999 Congress on Evolutionary Computation, CEC 1999, vol. 1, pp. 244–251. IEEE (1999)

12.

Kaboudan, M.A.: Genetic programming prediction of stock prices. Comput. Econ. 16(3), 207–236 (2000)CrossRef

13.

Kampouridis, M., Tsang, E.: EDDIE for investment opportunities forecasting: extending the search space of the GP. In: 2010 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8. IEEE (2010)

14.

Koza, J.R.: Genetic programming as a means for programming computers by natural selection. Stat. Comput. 4(2), 87–112 (1994)CrossRef

15.

Li, J.: FGP: a genetic programming based tool for financial forecasting. Ph.D. thesis, University of Essex (2000)

16.

Maden, İ., Uyar, A., Ozcan, E.: Landscape analysis of simple perturbative hyperheuristics. In: Mendel, vol. 2009, p. 15th (2009)

17.

Nyathi, T., Pillay, N.: Automated design of genetic programming classification algorithms using a genetic algorithm. In: Squillero, G., Sim, K. (eds.) EvoApplications 2017. LNCS, vol. 10200, pp. 224–239. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-55792-2_15CrossRef

18.

Nyathi, T., Pillay, N.: Comparison of a genetic algorithm to grammatical evolution for automated design of genetic programming classification algorithms. Expert Syst. Appl. 104, 213–234 (2018)

19.

Ochoa, G., Qu, R., Burke, E.K.: Analyzing the landscape of a graph based hyper-heuristic for timetabling problems. In: Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, pp. 341–348. ACM (2009)

20.

Otero, F.E.B., Kampouridis, M.: A comparative study on the use of classification algorithms in financial forecasting. In: Esparcia-Alcázar, A.I., Mora, A.M. (eds.) EvoApplications 2014. LNCS, vol. 8602, pp. 276–287. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-45523-4_23CrossRef

21.

Ryan, C., Collins, J.J., Neill, M.O.: Grammatical evolution: Evolving programs for an arbitrary language. In: Banzhaf, W., Poli, R., Schoenauer, M., Fogarty, T.C. (eds.) EuroGP 1998. LNCS, vol. 1391, pp. 83–96. Springer, Heidelberg (1998). https://doi.org/10.1007/BFb0055930CrossRef

22.

Tapia, M.G.C., Coello, C.A.C.: Applications of multi-objective evolutionary algorithms in economics and finance: a survey. In: IEEE Congress on Evolutionary Computation, CEC 2007, pp. 532–539. IEEE (2007)

23.

Tsang, E.P., Li, J., Butler, J.M.: EDDIE beats the bookies. Softw. Pract. Exp. 28(10), 1033–1043 (1998)

24.

Wang, P., Tsang, E.P., Weise, T., Tang, K., Yao, X.: Using GP to evolve decision rules for classification in financial data sets. In: 2010 9th IEEE International Conference on Cognitive Informatics (ICCI), pp. 720–727. IEEE (2010)

Titel: Automated Design of Genetic Programming Classification Algorithms for Financial Forecasting Using Evolutionary Algorithms
verfasst von: Thambo Nyathi
Nelishia Pillay
Verlag: Springer International Publishing
Buch: Theory and Practice of Natural Computing
Print ISBN: 978-3-030-04069-7

Electronic ISBN: 978-3-030-04070-3

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-030-04070-3_16

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"