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
Genetic Algorithm: Reviews, Implementations, and Applications
