Genetic Algorithm: Reviews, Implementations, and Applications

Abstract

Nowadays genetic algorithm (GA) is greatly used in engineering pedagogy as an adaptive technique to learn and solve complex problems and issues. It is a meta-heuristic approach that is used to solve hybrid computation challenges. GA utilizes selection, crossover, and mutation operators to effectively manage the searching system strategy. This algorithm is derived from natural selection and genetics concepts. GA is an intelligent use of random search supported with historical data to contribute the search in an area of the improved outcome within a coverage framework. Such algorithms are widely used for maintaining high-quality reactions to optimize issues and problems investigation. These techniques are recognized to be somewhat of a statistical investigation process to search for a suitable solution or prevent an accurate strategy for challenges in optimization or searches. These techniques have been produced from natural selection or genetics principles. For random testing, historical information is provided with intelligent enslavement to continue moving the search out from the area of improved features for processing of the outcomes. It is a category of heuristics of evolutionary history using behavioral science-influenced methods like an annuity, gene, preference, or combination (sometimes refers to as hybridization). This method seemed to be a valuable tool to find solutions for problems optimization. In this paper, the author has explored the GAs, its role in engineering pedagogies, and the emerging areas where it is using, and its implementation.

Application of Genetic Algorithms to Optimization of Medium and Heavy Rotating Equipment Foundations

“…Literature study shows successful application of Genetic Algorithms to various design applications [7]. Identified machine foundation design is the area where there is scope for optimization and simplifying the search for practicable solution.…”

“…It is also used to optimize the configuration of machine foundations. Some of the examples from literature are discussed in this report for demonstrating genetic algorithms applications in civil engineering field [7].…”

Algoritmos Genéticos aplicado ao mundo de Wumpus: uma comparação entre agentes baseados em regras e agentes inteligentes

“…É uma meta-heurística pertencente a uma larga classe de algoritmos evolucionários na informática e matemática computacional. São frequentemente utilizados para a criac ¸ão de soluc ¸ões de alta qualidade em problemas de busca e otimizac ¸ão, focando em operadores bioinspirados como: reproduc ¸ão, cruzamento e mutac ¸ão [Whitley 1994], [Alam et al 2020] e [Sivaraj and Ravichandran 2011].…”

“…Esse processo assegura que a nova gerac ¸ão seja totalmente nova, porém, possuindo característica de seus antecedentes para manter sua capacidade de adaptac ¸ão adquirida nas gerac ¸ões anteriores, por meio do cruzamento [Alam et al 2020], além da inserc ¸ão de características aleatórias por meio da mutac ¸ão, com a finalidade de manter a heterogeneidade [Sivaraj and Ravichandran 2011].…”

Digital processing of images with defects due to double beam attenuation in indirect X-ray fluorescence mapping

“…To do that, images were artificially generated and added ghosting to train a computational procedure so as to evaluate on how well it performs in removing the defect. The process is a rudimentary genetic algorithm [7], built specifically for this problem, and which reached satisfactory conclusions.…”