Differential Evolution Control Parameters Study for Self-Adaptive Triangular Brushstrokes
Abstract
This paper proposes a lossy image representation where a reference image is approximated by an evolved image, constituted of variable number of triangular brushstrokes. The parameters of each triangle brush are evolved using differential evolution, which self-adapts the triangles to the reference image, and also self-adapts some of the control parameters of the optimization algorithm, including the number of triangles. Experimental results show the viability of the proposed encoding and optimization results on a few sample reference images. The results of the self-adapting control parameters for crossover and mutation in differential evolution are also compared to results with keeping these parameters constant, like in a basic differential evolution algorithm. Statistical tests are furthermore included to confirm the improved performance with the self-adaptation of the control parameters over the fixed control parameters.
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