Genetic Algorithm: Reviews, Implementations, and Applications

International Journal of Engineering Pedagogy, 2020

19 Pages Posted: 4 Sep 2020

See all articles by Tanweer Alam

Tanweer Alam

Islamic University of Madinah

Shamimul Qamar

King Khalid University

Amit Dixit

Quantum School of Technology - Quantum Global Campus

Mohamed Benaida

Islamic University of Madinah

Date Written: July 26, 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 man-age 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.

Suggested Citation

Alam, Tanweer and Qamar, Shamimul and Dixit, Amit and Benaida, Mohamed, Genetic Algorithm: Reviews, Implementations, and Applications (July 26, 2020). International Journal of Engineering Pedagogy, 2020, Available at SSRN: https://ssrn.com/abstract=3660827

Tanweer Alam (Contact Author)

Islamic University of Madinah ( email )

Madinah Al-Munawwara
Saudi Arabia

Shamimul Qamar

King Khalid University ( email )

Saudi Arabia
KING KHALID UNIVERSITY
Abha, 61413
Saudi Arabia

Amit Dixit

Quantum School of Technology - Quantum Global Campus ( email )

Roorkee
India

Mohamed Benaida

Islamic University of Madinah

Madinah Al-Munawwara
Saudi Arabia
170 (Fax)

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