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

Abstract

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.

Genetic Algorithm for Feature Selection Applied to Financial Time Series Monotonicity Prediction: Experimental Cases in Cryptocurrencies and Brazilian Assets

“…In future developments, we intend to modify our material in order to make it effective in detecting values in financial time series and not just in detecting monotonicity. Furthermore, more elaborate metaheuristics, such as those based on massive local search [ 32 , 33 , 34 , 35 , 36 , 37 ], should be considered in feature selection.…”

A New Frequency Analysis Operator for Population Improvement in Genetic Algorithms to Solve the Job Shop Scheduling Problem

“…For future advances and developments, we intend to consider deep-learning techniques, mainly reinforcement-learning methods, to detect genetic influences on chromosomes from a GA-like method population. Furthermore, we intend to expand the devel-oped material to other problems in the same field of application, such as Flexible Job Shop Scheduling [62] and to other classes of problems that demand combinatorial optimization, such as pseudo-colorization problems in graphs [21,22].…”

“…More specifically, it is possible to highlight certain disadvantages in the use of GA in solving COPs [20,21]. In detail, it is common for this set of techniques to become stagnant [22], during their iterations in solutions that are local minimums, which configures the phenomenon known as premature convergence [23]. Furthermore, GAs may require high computational time [24] to obtain good solutions to this type of problem.…”

An RGB Pseudo-Colorization Method for Filtering of Multi-Source Graphical Data

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