Abstract
In search, optimisation and simulation applications, model building is largely manual. However, it may be automated if a complete enough body of data is available. The objective of the present work is to generate models for use in decision support.
In the following we shall concentrate on the method, namely genetic programming, suitable for our problem. A review of genetic programming and a description of an implementation of a tool for symbolic regression is given. Limited experimental results are reported and future improvements to the tool are also discussed.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
L. Altenberg. The evolution of evolvability in genetic programming. In K.E. Kinnear, editor, Advances in Genetic Programming. MIT Press, MA., USA, 1994.
P.J. Angeline. Genetic programming and emergent intelligence. In K.E. Kinnear, editor, Advances in Genetic Programming. MIT Press, 1994.
R. Axelrod.The Evolution of Cooperation. Basic Books, New York, 1984.
R. Axelrod. The evolution of strategies in the iterated prisoners’ dilemma. In L. Davis, editor, Genetic Algorithms and Simulated Annealing, pages 32–42. Morgan Kaufmann, Los Altos, Calif., USA, 1987.
J.S. Collins. A regression analysis program incorporating heuristic term selection. In E. Dale and D. Michie, editors, Machine Intelligence 2. Elsevier, 1968.
K. De Jong. On using genetic algorithms to search program spaces. In J.J. Grefenstette, editor, Genetic Algorithms and their Applications: Proceedings of the 2nd International Conference on Genetic Algorithms. Lawrence Erlbaum Associates, 1987.
G.J. Deboek, editor. Trading on the Edge: Neural, Genetic, and Fuzzy Systems for Chaotic Financial Markets. Wiley and Sons, 1994.
P. D’Haeseleer and J. Bluming. Effects of locality on individual and population evolution. In K.E. Kinnear, editor, Advances in Genetic Programming. MIT Press, MA., USA, 1994.
D. Foot. Operational Urban Models. Methuen and Co. Ltd, London, 1981.
E.D. Goldberg. Simple genetic algorithms and the minimal, deceptive problem. In L. Davis, editor, Genetic Algorithms and Simulated Annealing, pages 75–88. Morgan Kaufmann, Los Altos, Calif., USA, 1987.
E.D. Goldberg. Genetic Algorithms in Search, Optimisation and Machine Learning. Addison Wesley, 1989.
S. Goonatilake and J.A. Campbell. Genetic-fuzzy hybrid systems for finacial decision making, 1995. Working Paper.
I. Harvey. Species adaptation genetic algorithms: A basis for a continuing saga. In F.J. Varela and P. Bourgine, editors,Proceedings of the first European Conference on Artificial Life, (ECAL). MIT Pess, 1992.
J.H. Holland.Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, Michigan, USA, 1974.
K.E. Kinnear, editor. Advances in Genetic Programming. MIT Press, Mass., USA, 1994.
J. Koza and D. Andre. Parallel genetic programming on a network of transputers. Technical Report CS-TR-95–1542, Dept. Comp. Science, University of Stanford, Jan. 1995.
J.R. Koza. Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge, England, 1993.
J.R. Koza. Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Press, Cambridge, England, 1994.
J.R. Koza. Genetic programming for economic modelling. In S. Goonatilake and P. Treleaven, editors, Intelligent Systems for Finance and Business, pages 251–269. John Wiley & Sons Ltd, 1995.
J.R. Koza and J.P. Rice. Genetic programming: The movie, 1992.
W.B. Langdon. Pareto, population partitioning, price and genetic programmimg, 1995. Research Notes.
W.B Langdon and A. Qureshi. Genetic programming: Computers using “Natural Selection” to generate programs, 1995. Working Paper.
Z. Michalewicz.Genetic Algorithms + Data Structures = Evolution Algorithms. Springer Verlag, London, second, extended edition edition, 1994.
U.-M. O’Reilly.An Analysis of Genetic Programmimg. PhD thesis, School of Computer Studies, Carleton University, Ottawa, Ontario, 1995.
U.-M. O’Reilly and F. Oppacher. The troubling aspects of a building block hypothesis for genetic programming, 1992. Working Paper.
N.H. Packard. A genetic learning algorithm for the analysis of complex data. Complex Systems, 4:543–572, 1990.
P.M. Pardalos and J.B. Rosen. Constrained Global Optimization: Algorithms and Applications. Springer Verlag, Berlin, 1987. Lecture Notes in Computer Science no.268.
J.E. Perry. The effect of population enrichment in genetic programming. In Proceedings of the 1994 IEEE World Congress on Computational Intelligence. IEEE Press, 1994.
I. Rechenberg. Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. Frommann-Holzboog Verlag, Stuttgart, 1973.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1996 Springer-Verlag London Limited
About this paper
Cite this paper
Salhi, A., Glaser, H., De Roure, D. (1996). Model Generation Using Genetic Programming. In: Jesshope, C., Shafarenko, S. (eds) UK Parallel ’96. Springer, London. https://doi.org/10.1007/978-1-4471-1504-5_7
Download citation
DOI: https://doi.org/10.1007/978-1-4471-1504-5_7
Publisher Name: Springer, London
Print ISBN: 978-3-540-76068-9
Online ISBN: 978-1-4471-1504-5
eBook Packages: Springer Book Archive