  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.
