Abstract
This work analyzes fitness landscapes for the image filter design problem approached using functional-level Cartesian Genetic Programming. Smoothness and ruggedness of fitness landscapes are investigated for five genetic operators. It is shown that the mutation operator and the single-point crossover operator generate the smoothest landscapes and thus they are useful for practical applications in this area. In contrast to the gate-level evolution, a destructive behavior of a simple crossover operator has not been confirmed.
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Slaný, K., Sekanina, L. (2007). Fitness Landscape Analysis and Image Filter Evolution Using Functional-Level CGP. 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_29
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DOI: https://doi.org/10.1007/978-3-540-71605-1_29
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