Genetic Algorithms (GAs) are known to be valuable tools for optimization purposes. In general, GAs can find good solutions by setting their configuration parameters, such as mutation and crossover rates, population size, etc., to standard (i.e., widely used) values. In some application domains, changing the values of these parameters does not improve the quality of the solution, but might influence the ability of the algorithm to find such solution. In other application domains, fine tuning these parameters could result into a significant improvement of the solution quality. In this paper we present an experimental study aimed at finding how fine tuning the parameters of a GA used for the insertion of a fragile watermark into a bitmap image influences the quality of the resulting digital object. However, when proposing a GA based new tool to non-expert users, selecting the best parameter setting is not an easy task. Therefore, we will suggest how to automatically set the GA parameters in order to meet the quality and/or running time performances requested by the user.

Automatic Selection of GA Parameters for Fragile Watermarking

BOTTA, Marco;CAVAGNINO, Davide;POMPONIU, VICTOR
2014

Abstract

Genetic Algorithms (GAs) are known to be valuable tools for optimization purposes. In general, GAs can find good solutions by setting their configuration parameters, such as mutation and crossover rates, population size, etc., to standard (i.e., widely used) values. In some application domains, changing the values of these parameters does not improve the quality of the solution, but might influence the ability of the algorithm to find such solution. In other application domains, fine tuning these parameters could result into a significant improvement of the solution quality. In this paper we present an experimental study aimed at finding how fine tuning the parameters of a GA used for the insertion of a fragile watermark into a bitmap image influences the quality of the resulting digital object. However, when proposing a GA based new tool to non-expert users, selecting the best parameter setting is not an easy task. Therefore, we will suggest how to automatically set the GA parameters in order to meet the quality and/or running time performances requested by the user.
Evo*2014
Granada, Spain
23-25 April 2014
Application s Evolutionary Computation
Springer-Verlag
8602
526
537
978-3-662-45522-7
fragile image watermarking; genetic algorithms
Marco Botta; Davide Cavagnino; Victor Pomponiu
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/152375
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