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Open AccessArticle

An Improved Local Search Genetic Algorithm with a New Mapped Adaptive Operator Applied to Pseudo-Coloring Problem

1
Department of Computing, Federal University of São Carlos, São Carlos SP 13565-905, Brazil
2
Department of Applied Mathematics and Statistics, University of São Paulo, São Carlos SP 13566-590, Brazil
*
Author to whom correspondence should be addressed.
This paper is an extended version of our preliminary work “Contreras, et al. 2020”.
Symmetry 2020, 12(10), 1684; https://doi.org/10.3390/sym12101684
Received: 29 August 2020 / Revised: 22 September 2020 / Accepted: 30 September 2020 / Published: 14 October 2020
(This article belongs to the Special Issue Symmetry in Vision Ⅱ)
In many situations, an expert must visually analyze an image arranged in grey levels. However, the human eye has strong difficulty in detecting details in this type of image, making it necessary to use artificial coloring techniques. The pseudo-coloring problem (PsCP) consists of assigning to a grey-level image, pre-segmented in K sub-regions, a set of K colors that are as dissimilar as possible. This problem is part of the well-known class of NP-Hard problems and, therefore, does not present an exact solution for all instances. Thus, meta-heuristics has been widely used to overcome this problem. In particular, genetic algorithm (GA) is one of those techniques that stands out in the literature and has already been used in PsCP. In this work, we present a new method that consists of an improvement of the GA specialized in solving the PsCP. In addition, we propose the addition of local search operators and rules for adapting parameters based on symmetric mapping functions to avoid common problems in this type of technique such as premature convergence and inadequate exploration in the search space. Our method is evaluated in three different case studies: the first consisting of the pseudo-colorization of real-world images on the RGB color space; the second consisting of the pseudo-colorization in RGB color space considering synthetic and abstract images in which its sub-regions are fully-connected; and the third consisting of the pseudo-colorization in the Munsell atlas color set. In all scenarios, our method is compared with other state-of-the-art techniques and presents superior results. Specifically, the use of mapped automatic adjustment operators proved to be powerful in boosting the proposed meta-heuristic to obtain more robust results in all evaluated instances of PsCP in all the considered case studies. View Full-Text
Keywords: adaptive operator; genetic algorithm; local search; pseudo-coloring problem; visualization adaptive operator; genetic algorithm; local search; pseudo-coloring problem; visualization
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MDPI and ACS Style

Viana, M.S.; Morandin Junior, O.; Contreras, R.C. An Improved Local Search Genetic Algorithm with a New Mapped Adaptive Operator Applied to Pseudo-Coloring Problem. Symmetry 2020, 12, 1684. https://doi.org/10.3390/sym12101684

AMA Style

Viana MS, Morandin Junior O, Contreras RC. An Improved Local Search Genetic Algorithm with a New Mapped Adaptive Operator Applied to Pseudo-Coloring Problem. Symmetry. 2020; 12(10):1684. https://doi.org/10.3390/sym12101684

Chicago/Turabian Style

Viana, Monique S.; Morandin Junior, Orides; Contreras, Rodrigo C. 2020. "An Improved Local Search Genetic Algorithm with a New Mapped Adaptive Operator Applied to Pseudo-Coloring Problem" Symmetry 12, no. 10: 1684. https://doi.org/10.3390/sym12101684

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