Abstract
In this paper we present an empirical comparison between evolutionary representations for the resolution of the inverse problem for iterated function systems (IFS). We introduce a class of problem instances that can be used for the comparison of the inverse IFS problem as well as a novel technique that aids exploratory analysis of experiment data. Our comparison suggests that representations that exploit problem specific information, apart from quality/fitness feedback, perform better for the resolution of the inverse problem for IFS.
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References
Back, T.: Evolutionary Algorithms in Theory and Practice. Oxford University Press, Oxford (1996)
Barnsley, M.F., Hurd, L.P.: Fractal Image Compression. AK Peters, Wellesley (1993)
Coen, E.: The Art of Genes. Oxford University Press, Oxford (1999)
Collet, P., Lutton, E., Raynal, F., Schoenauer, M.: Polar ifs + parisian genetic programming = efficient ifs inverse problem solving. Genetic Programming and Evolvable Machines Journal 1(4), 339–361 (2000)
Hutchinson, J.E.: Fractals and self-similarity. Indiana University Journal 35(5), 713–747 (1981)
Koosis, D.J.: Statistics, a Self-Teaching Guide. John Wiley & Sons, New York (1997)
Lu, N.: Fractal Imaging. Academic Press, San Diego (1997)
Lutton, E., Cretin, G., Levy-Vehel, J., Glevarec, P., Roll, C.: Mixed ifs: resolution of the inverse problem using genetic programming. Complex Systems 9(5), 375–398 (1995)
Nettleton, D.J., Garigliano, R.: Evolutionary algorithms and the construction of fractals: solution of the inverse problem. Biosystems 33, 221–231 (1994)
Nettleton, D.J., Garigliano, R.: Reductions in the search space for deriving a fractal set of an arbitrary shape. Mathematical Imaging and Vision 6(4), 379–393 (1996)
Sarafopoulos, A.: Automatic generation of affine ifs and strongly typed genetic programming. In: Langdon, W.B., Fogarty, T.C., Nordin, P., Poli, R. (eds.) EuroGP 1999. LNCS, vol. 1598, pp. 149–160. Springer, Heidelberg (1999)
Sarafopoulos, A.: Evolution of affine transformations and iterated function systems using hierarchical evolution strategy. In: Miller, J., Tomassini, M., Lanzi, P.L., Ryan, C., Tetamanzi, A.G.B., Langdon, W.B. (eds.) EuroGP 2001. LNCS, vol. 2038, pp. 176–191. Springer, Heidelberg (2001)
Sarafopoulos, A., Buxton, B.: Resolution of the inverse problem for iterated function system using evolutionary algorithms. In: Proceedings of the 2006 IEEE Congress on Evolutionary Computation. IEEE World Congress on Computational Intelligence, pp. 3816–3823. IEEE Press, Los Alamitos (2006)
Schwefel, P.H.: Evolution and Optimum Seeking. John Wiley & Sons, New York (1995)
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Sarafopoulos, A., Buxton, B. (2007). Empirical Comparison of Evolutionary Representations of the Inverse Problem for Iterated Function Systems. In: Ebner, M., O’Neill, M., Ekárt, A., Vanneschi, L., Esparcia-Alcázar, A.I. (eds) Genetic Programming. EuroGP 2007. Lecture Notes in Computer Science, vol 4445. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71605-1_7
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DOI: https://doi.org/10.1007/978-3-540-71605-1_7
Publisher Name: Springer, Berlin, Heidelberg
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