Egipsys: An enhanced gene expression programming approach for symbolic regression problems

• Autorzy: Heitor S. Lopes , Wagner R. Weinert
• Języki publikacji: EN

Abstrakty

EN

This paper reports a system based on the recently proposed evolutionary paradigm of gene expression programming (GEP). This enhanced system, called EGIPSYS, has features specially suited to deal with symbolic regression problems. Amongst the new features implemented in EGIPSYS are: new selection methods, chromosomes of variable length, a new approach to manipulating constants, new genetic operators and an adaptable fitness function. All the proposed improvements were tested separately, and proved to be advantageous over the basic GEP@. EGIPSYS was also applied to four difficult identification problems and its performance was compared with a traditional implementation of genetic programming (LilGP). Overall, EGIPSYS was able to obtain consistently better results than the system using genetic programming, finding less complex solutions with less computational effort. The success obtained suggests the adaptation and extension of the system to other classes of problems.

Słowa kluczowe

EN

mathematical modeling; systems identification; symbolic regression; evolutionary computation

• Wydawca: University of Zielona Gora Press
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2004
• Tom: 14
• Numer: 3
• Strony: 375-384

Daty

wydano

2004

otrzymano

2004-03-01

poprawiono

2004-06-01

Twórcy

autor

Heitor S. Lopes

• Centro Federal de Educação Tecnológica do Paraná/CPGEI, Av. 7 de setembro, 3165, 80230901 Curitiba (PR), Brazil

autor

Wagner R. Weinert

• Centro Federal de Educação Tecnológica do Paraná/CPGEI, Av. 7 de setembro, 3165, 80230901 Curitiba (PR), Brazil

Bibliografia

• Ferreira C. (2001): Gene Expression Programming: A new adaptive algorithm for solving problems. - Complex Systems, Vol. 13, No. 2, pp. 87-129.
• Ferreira C. (2003): Function finding and a creation of numerical constants in gene expression programming, In: Advances in Soft Computing, Engineering Design and Manufacturing (J.M. Benitez, O. Cordon, F. Hoffmann and R. Roy, Eds.). - Springer-Verlag: Berlin, pp. 257-266.
• Fogel L.J., Owens A.J. and Walsh M.J. (1966): Artificial Intelligence Through Simulated Evolution. - New York: Wiley
• Goldberg D.E. (1989): Genetic Algorithms in Search, Optimization and Machine Learning. - Reading: Addison-Wesley.
• Guidorzi R.P. and Rossi P. (1974): Identification of a power plant from normal operating records. - Automat. Contr. Theory Applic., Vol. 2, No. 1, pp. 63-67.
• Guidorzi R.P., Losito M.P. and Muratori T. (1980): On the last eigenvalue test in the structural identification of linear multivariable systems. - Proc. 5th Europ. Meeting Cybernetics and Systems Research, Vienna, pp. 217-228.
• Hoai N.X., McKay R.I., Essam D. and Chau R. (2002): Solving the symbolic regression problem with tree-adjunct grammar guided genetic programming: The comparative results. - Proc. 2002 Congress on Evolutionary Computation, Honolulu, USA, Vol. 2, pp. 1326-1331.
• Holland J.H. (1995): Adaptation in Natural and Artificial Systems. - Ann Arbor: The University of Michigan Press.
• Koza J.R. (1992): Genetic Programming: On the Programming of Computers by Means of Natural Selection. - Cambridge: MIT Press.
• Koza J.R. (1994): Genetic Programming II: Automatic Discovery of Reusable Programs. - Cambridge: MIT Press, 1994.
• McAvoy T.J., Hsu E. and Lowenthal S. (1972): Dynamics of pH in controlled stirred tank reactor. - Ind. Eng. Chem. Process Des. Develop., Vol. 11, No. 1, pp. 71-78.
• Moonen M., De Moor B., Vandenberghe L. and Vandewalle J. (1989): On- and off-line identification of linear state-space models. -Int. J. Contr., Vol. 49, No. 2, pp. 219-0232.
• Rechenberg I. (1973): Evolutions strategie: Optimierung Technischer Systemen nach Prinzipien der Biologischen Evolution. - Stuttgart: Frommann-Holzboog Verlag.
• Salhi A., Glaser H. and DeRoure D. (1998): Parallel implementation of a genetic-programming based tool for symbolic regression. - Inf. Process. Lett., Vol. 66, pp. 299-307.
• Schwefel H-P. (1977): Numerische Optimierung von Computer-Modellen mittels der Evolutionsstrategie. - Basel: Birkhauser.
• Shengwu X., Weinu W. and Feng L. (2003): A new genetic programming approach in symbolic regression. - Proc. 15th IEEE Int. Conf. Tools with Artificial Intelligence, Sacramento, USA, pp. 161-165.
• Weigend A.S., Huberman B.A. and Rumelhart D.E. (1992): Predicting sunspots and exchange rates with connectionist networks, In: Nonlinear Modeling and Forecasting (S. Eubank and M. Casdagli, Eds.). - Redwood City: Addison-Wesley, pp. 395-432.
• Zongker D., Punch B. and Rand B. (1998): Lilgp 1.1User's Manual. - Lansing: Michigan State University.