A New Local Search Adaptive Genetic Algorithm for the Pseudo-Coloring Problem

Contreras, Rodrigo Colnago; Morandin Junior, Orides; Viana, Monique Simplicio

Several applications result in a gray level image partitioned into different regions of interest. However, the human brain has difficulty in recognizing many levels of gray. In some cases, this problem is alleviated with the attribution of artificial colors to these regions, thus configuring an application in the area of visualization and graphic processing responsible for categorizing samples using colors. However, the task of making a set of distinct colors for these regions stand out is a problem of the NP-hard class, known as the pseudo-coloring problem (PsCP). In this work, it is proposed to use the well-known meta-heuristic Genetic Algorithm together with operators specialized in the local search for solutions as well as self-adjusting operators responsible for guiding the parameterization of the technique during the resolution of PsCPs. The proposed methodology was evaluated in two different scenarios of color assignment, having obtained the best results in comparison to the techniques that configure the state of the art.