Tanweer Alam. Shamimul Qamar. Amit Dixit. Mohamed Benaida. ”
Genetic Algorithm: Reviews, Implementations, and Applications.”,
International Journal of Engineering Pedagogy (iJEP). 2020.
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.
Keywords— Genetic Algorithm, Search Techniques, Random Tests,
Evolution, Applications.