It is not uncommon for today’s problems to fall within the scope of the well-known class of NP-Hard problems. These problems generally do not have an analytical solution, and it is necessary to use meta-heuristics to solve them. The Job Shop Scheduling Problem (JSSP) is one of these problems, and for its solution, techniques based on Genetic Algorithm (GA) form the most common approach used in the literature. However, GAs are easily compromised by premature convergence and can be trapped in a local optima. To address these issues, researchers have been developing new methodologies based on local search schemes and improvements to standard mutation and crossover operators. In this work, we propose a new GA within this line of research. In detail, we generalize the concept of a massive local search operator; we improved the use of a local search strategy in the traditional mutation operator; and we developed a new multi-crossover operator. In this way, all operators of the proposed algorithm have local search functionality beyond their original inspirations and characteristics. Our method is evaluated in three different case studies, comprising 58 instances of literature, which prove the effectiveness of our approach compared to traditional JSSP solution methods.
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.
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