Abstract
Genetic programming (GP) is a variant of genetic algorithms where the data structures handled are trees. This makes GP especially useful for evolving functional relationships or computer programs, as both can be represented as trees. Symbolic regression is the determination of a function dependence y=g(x) that approximates a set of data points (x i, yi). In this paper the feasibility of symbolic regression with GP is demonstrated on two examples taken from different domains. Furthermore several suggested methods from literature are compared that are intended to improve GP performance and the readability of solutions by taking into account introns or redundancy that occurs in the trees and keeping the size of the trees small. The experiments show that GP is an elegant and useful tool to derive complex functional dependencies on numerical data.
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References
Tobias Blickle and Lothar Thiele. Genetic programming and redundancy. In J. Hopf, editor, Genetic Algorithms within the Framework of Evolutionary Computation (Workshop at KI-94, Saarbrücken), pages 33–38. Max-Planck-Institut für Informatik (MPI-I-94-241), 1994.
Tobias Blickle and Lothar Thiele. A comparison of selection schemes used in genetic algorithms (2. edition). Technical Report 11, Computer Engineering and Communication Networks Lab (TIK), Swiss Federal Institute of Technology (ETH) Zurich, Gloriastrasse 35, CH-8092 Zurich, Dec 1995.
Hitoshi Iba, Takio Kurita, Hugo de Garis, and Taisuke Sato. System identification using structured genetic algorithms. In Stefanie Forrest, editor, Proceedings of the Fifth International Conference on Genetic Algorithms, pages 279–286, San Mateo, CA, 1993. Morgan Kaufmann Publishers.
John R. Koza. Genetic programming: on the programming of computers by means of natural selection. The MIT Press, Cambridge, Massachusetts, 1992.
Peter Nordin and Wolfgang Banzhaf. Complexity compression and evolution. In L. Eshelman, editor, Proceedings of the Sixth International Conference on Genetic Algorithms (ICGA95), pages 310–317, San Francisco, CA, 1995. Morgan Kaufmann Publishers.
Peter Nordin, Frank Francone, and Wolfgang Banzhaf. Explicitly defined introns and destructive crossover in genetic programming. In Justinian P. Rosca, editor, Proceedings of the Workshop on Genetic Programming (at Twelfth Int. Conf. on Machine Learning), pages 6–22. Technical Report 95.2, University of Rochester, 1995.
Justinian P. Rosca and Dana H. Ballard. Genetic programming with adaptive representations. Technical Report 489, The University of Rochester, Computer Science Department, Rochester, New York 14267, 1994.
Walter Alden Tackett. Recombination, Selection, and the Genetic Construction of Computer Programs. PhD thesis, Faculty of the Graduate School, University of Southern California, 1994.
Byoung-Tak Zhang and Heinz Mühlenbein. Balancing accuracy and parsimony in genetic programming. Evolutionary Computation, 3(1):17–38, 1995.
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© 1996 Springer-Verlag Berlin Heidelberg
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Blickle, T. (1996). Evolving compact solutions in genetic programming: A case study. In: Voigt, HM., Ebeling, W., Rechenberg, I., Schwefel, HP. (eds) Parallel Problem Solving from Nature — PPSN IV. PPSN 1996. Lecture Notes in Computer Science, vol 1141. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61723-X_1020
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DOI: https://doi.org/10.1007/3-540-61723-X_1020
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