Abstract
Genetic programming is a powerful heuristic search technique that is used for a number of real world applications to solve amongst others regression, classification, and time-series forecasting problems. A lot of progress towards a theoretic description of genetic programming in form of schema theorems has been made, but the internal dynamics and success factors of genetic programming are still not fully understood. In particular, the effects of different crossover operators in combination with offspring selection are largely unknown.
This contribution sheds light on the ability of well-known GP crossover operators to create better offspring when applied to benchmark problems. We conclude that standard (sub-tree swapping) crossover is a good default choice in combination with offspring selection, and that GP with offspring selection and random selection of crossover operators can improve the performance of the algorithm in terms of best solution quality when no solution size constraints are applied.
The work described in this paper was done within HEUREKA!, the Josef Ressel center for heuristic optimization sponsored by the Austrian Research Promotion Agency (FFG).
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References
Affenzeller, M., Wagner, S.: Offspring selection: A new self-adaptive selection scheme for genetic algorithms. In: Adaptive and Natural Computing Algorithms. Springer Computer Series, pp. 218–221. Springer, Heidelberg (2005)
Affenzeller, M., Winkler, S.M., Wagner, S.: Effective allele preservation by offspring selection: An empirical study for the TSP. International Journal of Simulation and Process Modelling (2009) (accepted to appear)
Altenberg, L.: The evolution of evolvability in genetic programming. In: Kinnear Jr., K.E. (ed.) Advances in Genetic Programming, ch. 3, pp. 47–74. MIT Press, Cambridge (1994)
Asuncion, A., Newman, D.J.: UCI machine learning repository (2007)
Koza, J.R.: Genetic Programming. MIT Press, Cambridge (1992)
Kronberger, G., Winkler, S., Affenzeller, M., Wagner, S.: On crossover success rate in genetic programming with offspring selection. In: Vanneschi, L., Gustafson, S., Moraglio, A., De Falco, I., Ebner, M. (eds.) Genetic Programming, pp. 232–243. Springer, Heidelberg (2009)
Langdon, W.B.: Size fair and homologous tree genetic programming crossovers. Genetic Programming and Evolvable Machines 1(1/2), 95–119 (2000)
Langdon, W.B., Banzhaf, W.: Repeated patterns in genetic programming. Natural Computing (2008); Published online: May 26, 2007
Langdon, W.B., Poli, R.: Foundations of Genetic Programming. Springer, Heidelberg (2002)
Luke, S.: Two fast tree-creation algorithms for genetic programming. IEEE Trans. Evolutionary Computation 4(3), 274–283 (2000)
Murphy, G., Ryan, C.: Exploiting the path of least resistance in evolution. In: GECCO 2008: Proceedings of the 10th annual conference on Genetic and evolutionary computation, Atlanta, GA, USA, July 12-16, pp. 1251–1258. ACM, New York (2008)
Murphy, G., Ryan, C.: A simple powerful constraint for genetic programming. In: O’Neill, M., Vanneschi, L., Gustafson, S., Esparcia Alcázar, A.I., De Falco, I., Della Cioppa, A., Tarantino, E. (eds.) EuroGP 2008. LNCS, vol. 4971, pp. 146–157. Springer, Heidelberg (2008)
O’Reilly, U.-M., Oppacher, F.: Hybridized crossover-based search techniques for program discovery. In: Proceedings of the 1995 World Conference on Evolutionary Computation, Perth, Australia, 29 November -1 December, vol. 2, pp. 573–578. IEEE Press, Los Alamitos (1995)
Poli, R.: A simple but theoretically-motivated method to control bloat in genetic programming. In: Ryan, C., Soule, T., Keijzer, M., Tsang, E.P.K., Poli, R., Costa, E. (eds.) EuroGP 2003. LNCS, vol. 2610, pp. 204–217. Springer, Heidelberg (2003)
Poli, R., Langdon, W.B.: On the search properties of different crossover operators in genetic programming. In: Genetic Programming 1998: Proceedings of the Third Annual Conference, University of Wisconsin, Madison, Wisconsin, USA, July 22-25, pp. 293–301. Morgan Kaufmann, San Francisco (1998)
Poli, R., Langdon, W.B., McPhee, N.F.: A Field Guide to Genetic Programming. Lulu.com (2008)
Poli, R., McPhee, N.F.: General schema theory for genetic programming with subtree-swapping crossover: part I. Evol. Comput. 11(1), 53–66 (2003)
Poli, R., McPhee, N.F.: General schema theory for genetic programming with subtree-swapping crossover: part II. Evol. Comput. 11(2), 169–206 (2003)
Poli, R., McPhee, N.F., Rowe, J.E.: Exact schema theory and markov chain models for genetic programming and variable-length genetic algorithms with homologous crossover. Genetic Programming and Evolvable Machines 5(1), 31–70 (2004)
Poli, R., Rowe, J.E., Stephens, C.R., Wright, A.H.: Allele diffusion in linear genetic programming and variable-length genetic algorithms with subtree crossover. In: Foster, J.A., Lutton, E., Miller, J., Ryan, C., Tettamanzi, A.G.B. (eds.) EuroGP 2002. LNCS, vol. 2278, pp. 212–227. Springer, Heidelberg (2002)
Soule, T., Foster, J.A.: Code size and depth flows in genetic programming. In: Koza, J.R., Deb, K., Dorigo, M., Fogel, D.B., Garzon, M., Iba, H., Riolo, R.L. (eds.) Genetic Programming 1997: Proceedings of the Second Annual Conference, Stanford University, CA, USA, July 13-16, pp. 313–320. Morgan Kaufmann, San Francisco (1997)
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Kronberger, G., Winkler, S., Affenzeller, M., Beham, A., Wagner, S. (2009). On the Success Rate of Crossover Operators for Genetic Programming with Offspring Selection. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory - EUROCAST 2009. EUROCAST 2009. Lecture Notes in Computer Science, vol 5717. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04772-5_102
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DOI: https://doi.org/10.1007/978-3-642-04772-5_102
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