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

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

doi:10.1007/978-3-030-53956-6_31

Cited by 9 publications

(13 citation statements)

References 18 publications

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“…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].…”

Section: Discussionmentioning

confidence: 99%

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

Viana

Contreras

Morandin

2022

Sensors

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Job Shop Scheduling is currently one of the most addressed planning and scheduling optimization problems in the field. Due to its complexity, as it belongs to the NP-Hard class of problems, meta-heuristics are one of the most commonly used approaches in its resolution, with Genetic Algorithms being one of the most effective methods in this category. However, it is well known that this meta-heuristic is affected by phenomena that worsen the quality of its population, such as premature convergence and population concentration in regions of local optima. To circumvent these difficulties, we propose, in this work, the use of a guidance operator responsible for modifying ill-adapted individuals using genetic material from well-adapted individuals. We also propose, in this paper, a new method of determining the genetic quality of individuals using genetic frequency analysis. Our method is evaluated over a wide range of modern GAs and considers two case studies defined by well-established JSSP benchmarks in the literature. The results show that the use of the proposed operator assists in managing individuals with poor fitness values, which improves the population quality of the algorithms and, consequently, leads to obtaining better results in the solution of JSSP instances. Finally, the use of the proposed operator in the most elaborate GA-like method in the literature was able to reduce its mean relative error from 1.395% to 0.755%, representing an improvement of 45.88%.

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Section: Discussionmentioning

confidence: 99%

“…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].…”

Section: Introductionmentioning

confidence: 99%

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

Viana

Contreras

Morandin

2022

Sensors

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“…The second data-set contains three real objects, Real Cone 1, Real Plane, and the LCD-TFT Filter. These image sequences were originally in gray scale, but for the sake of this experiment they were pseudo-colored, [54]- [56]. The colors were chosen in the same way as for data-set 1.…”

Section: A Experimental Setupmentioning

confidence: 99%

Focus Measurement in Color Space for Shape From Focus Systems

et al. 2021

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Shape from Focus (SFF) has been studied extensively in computer vision for 3D shape and depth recovery. The first stage in SFF methods is to compute the focus value of every pixel by converting the colored images into gray scale and then apply the focus measure operator. Converting colored values in the images into gray scale values may lead to imprecise mapping of pixels with different colored values onto the same gray scale value, this affects the overall accuracy of the system. In a colored image, the focused pixels maintain a considerable color difference from their neighboring pixels as compared to the defocused ones, which are blended into their neighborhood. This article presents an alternative method to measure the degree of focus by directly processing colored images. The color differences of the neighbor pixels with respect to the central pixel are obtained and summed together, this is followed by calculating their spread. The sum and the spread are combined to measure the degree of focus of the pixel in consideration. The proposed focus measure is then used for shape recovery of various simulated and real objects and is compared with previous techniques. The comparison results show the proposed method has the highest correlation and smallest RMSE values confirming the effectiveness of using color images for shape recovery.

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“…In the same work, a new quantitative measure of performance comparison is also proposed, which is described in details in the next sections of our work. More recently, we proposed in our preliminary work [21] a new GA with local search and adaptive operators specialized in solving the PsCP. In this text, we present the expansion of this technique, in which the adaptive operator makes use of mapping functions to update the mutation and crossover rates during the method iterations.…”

Section: Related Workmentioning

confidence: 99%

“…This paper is an extended version of our preliminary work [21]. In this text, we generalize the adaptive rules of our technique using mapping functions that automatically update the parameterization of the meta-heuristic.…”

Section: Introductionmentioning

confidence: 99%

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

2020

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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.

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

Cited by 9 publications

References 18 publications

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

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

Focus Measurement in Color Space for Shape From Focus Systems

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

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