Abstract
In this paper, we carry out experimental investigations that complement recent theoretical investigations on the runtime of simple genetic programming algorithms [3, 7]. Crucial measures in these theoretical analyses are the maximum tree size that is attained during the run of the algorithms as well as the population size when dealing with multi-objective models. We study those measures in detail by experimental investigations and analyze the runtime of the different algorithms in an experimental way.
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Briest, P., Brockhoff, D., Degener, B., Englert, M., Gunia, C., Heering, O., Jansen, T., Leifhelm, M., Plociennik, K., Röglin, H., Schweer, A., Sudholt, D., Tannenbaum, S., Wegener, I.: Experimental Supplements to the Theoretical Analysis of EAs on Problems from Combinatorial Optimization. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) PPSN VIII. LNCS, vol. 3242, pp. 21–30. Springer, Heidelberg (2004)
Droste, S., Jansen, T., Wegener, I.: On the analysis of the (1+1) evolutionary algorithm. Theoretical Computer Science 276, 51–81 (2002)
Durrett, G., Neumann, F., O’Reilly, U.-M.: Computational complexity analysis of simple genetic programing on two problems modeling isolated program semantics. In: FOGA, pp. 69–80. ACM (2011)
Evolved Analytics LLC. DataModeler 8.0. Evolved Analytics LLC (2010)
Goldberg, D.E., O’Reilly, U.-M.: Where Does the Good Stuff Go, and Why? How Contextual Semantics Influences Program Structure in Simple Genetic Programming. In: Banzhaf, W., Poli, R., Schoenauer, M., Fogarty, T.C. (eds.) EuroGP 1998. LNCS, vol. 1391, pp. 16–36. Springer, Heidelberg (1998)
Lässig, J., Sudholt, D.: Experimental Supplements to the Theoretical Analysis of Migration in the Island Model. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds.) PPSN XI. LNCS, vol. 6238, pp. 224–233. Springer, Heidelberg (2010)
Neumann, F.: Computational complexity analysis of multi-objective genetic programming. In: GECCO. ACM (to be published, 2012); arxiv.org: CoRR abs/1203.4881
Poli, R., Langdon, W.B., McPhee, N.F.: A Field Guide to Genetic Programming. lulu.com (2008)
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Urli, T., Wagner, M., Neumann, F. (2012). Experimental Supplements to the Computational Complexity Analysis of Genetic Programming for Problems Modelling Isolated Program Semantics. In: Coello, C.A.C., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds) Parallel Problem Solving from Nature - PPSN XII. PPSN 2012. Lecture Notes in Computer Science, vol 7491. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32937-1_11
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DOI: https://doi.org/10.1007/978-3-642-32937-1_11
Publisher Name: Springer, Berlin, Heidelberg
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